Dr Alexander Mikhalev
Managing Partner and CTO | Applied AI and Autonomous Delivery
Swindon, United Kingdom
LinkedIn · GitHub · metacortex.engineer
Profile
Managing Partner, CTO and Head of AI combining technology direction with hands-on engineering in Rust, Python and TypeScript. Builds AI capability and the software platforms that put it to work: industrial monitoring and anomaly detection, knowledge search, client applications and autonomous engineering infrastructure. Experience spans startup team building, enterprise architecture at Nationwide and applied research at Cranfield. Leads delivery while remaining close to the code, data and engineering decisions.
Current leadership
Zestic AI
Managing Partner | CTO and Head of AI | July 2023–present
- Set technology direction and delivery approach across the client portfolio, combining hands-on platform engineering with reusable AI infrastructure and staged engineering reviews.
- Shape a services-and-platform strategy that turns client delivery into reusable capabilities; connect architecture choices, pilot scope and delivery priorities to the wider business model.
- Confidential digital-learning provider: lead architecture and engineering across assessment services, web applications and data models; implement assessment evidence handling, learner-journey testing, analytics instrumentation and CI/CD.
- Confidential impact-finance lender: engineer borrower workflows, financial assessment, document generation and CRM integration, bringing operational lending processes into a connected software platform.
- Build TruthForge, a Rust-based narrative-analysis application, with supporting deployment and website delivery.
Platforms built and operated
Terraphim AI
Architecture and hands-on development | Independent open-source platform
- Lead development of a Rust/WebAssembly knowledge-search platform combining role-specific knowledge graphs, deterministic text matching, command-line tools and Model Context Protocol integration.
- Build local-first, privacy-preserving search and context-engineering capabilities that connect domain knowledge, session history and reusable learning to AI tools.
- Develop Terraphim’s distinctive graph-embedding approach in Rust, using curated concepts, synonym mappings and graph co-occurrence to rank documents and expose the relationships behind search results.
Autonomous delivery factory
AI Dark Factory and AgentForge
- Built and operate the AI Dark Factory, connecting issue prioritisation and agent execution with separate review, verification and validation gates across development projects.
- Extended AgentForge, a Gitea-based development platform, with scoped agent identity, signed audit events, a unified event stream and branch-based collaboration.
- Connected the development platform to a kanban workflow through a Rust bridge, making task coordination and review part of the engineering system.
Other concurrent appointments
Solutions Architect, InfinyOn | Contract | December 2022–December 2023
Director, Applied Knowledge Systems | Self-employed | December 2022–present
Industrial AI leadership
Klarian
Chief Technology Officer | November 2023–June 2025
AI/ML Architect | December 2022–December 2023
- Led AI and technology development for industrial pipeline monitoring and analytics, combining engineering models and machine learning to investigate pump efficiency and equipment anomalies for UK pipeline operators.
- Co-authored published client case studies on pump-performance analysis and orifice-plate anomaly detection, connecting sensor data and physical behaviour to operational decisions.
- Co-authored analysis identifying potential energy-cost reduction of approximately £5,000 per month per average mainline pump in the examined network section, conditional on restoring efficiency to as-new levels.
Earlier leadership
Nationwide Building Society
Architecture and prototyping leadership | December 2014–December 2022
AI/ML Architect, January 2019–December 2022; Head of Prototyping Engineering, January 2018–January 2019; Solutions Architect, Accelerated Change Efficiency and DevOps, January 2017–January 2018; Tech Lead, December 2014–January 2017.
- Established and ran a prototyping engineering lab; developed distributed-infrastructure prototypes and contributed to AI and blockchain strategy. Sponsored and mentored the team member whose work resulted in a distributed-data-storage patent.
- Led teams of 2–12 technical architects across parallel programmes including mobile banking, Open Banking, strategic origination, information management and regulatory change.
- Set technical quality assurance and governance for supplier designs and build deliverables, including IBM and Accenture; aligned project architecture with enterprise strategy and challenged technical debt.
- Led the Synthetic Data Platform from inception to enterprise capability to support data-driven innovation and reduce reliance on live data.
- Developed privacy-preserving machine-learning and digital-twin approaches.
- Shaped an enterprise DevOps blueprint and demonstrated architecture as code through a proof of concept; proposed a common business-value measure for change delivery.
M2M Ecosphere
Director of R&D | January 2019–January 2021 | Concurrent appointment
- Established innovation processes, an R&D ecosystem, knowledge management and organisation design to support innovation across Group Olvia.
- Deployed an AI prototype for the Group’s copper supply chain from the Democratic Republic of the Congo to China, addressing route optimisation and response to unexpected disruption.
Shopitize
Head of Architecture and Development | November 2011–December 2014
- Served as the most senior technology professional, aligning business strategy, product development and platform architecture. Managed up to 32 engineers and designers across four locations, then hired and built an in-house team.
- Designed and oversaw automated receipt processing using image processing and OCR; owned architecture spanning data processing, backend services, mobile APIs and a mix of public/private cloud infrastructure.
- Designed REST APIs, publish/subscribe processing and payment integrations with BACS and PayPal; built a product-matching search engine to replace Solr.
- Mentored data scientists and Science to Data Science participants.
Research and specialist delivery
Cranfield University
Research Fellow | May 2007–January 2012
- Developed wavelet-packet methods for mobile spectrum sharing and implemented the waveform on Anritsu vector signal generation and analysis hardware.
- Researched communications for in-vehicle sensor networks in an aerospace health-monitoring programme supported by an industry consortium led by Rolls-Royce and Boeing.
- Developed image-processing and data-fusion methods for emitter geolocation, comparing Hough-transform approaches with particle filtering in doctoral research.
Selected applied AI engagements
- Memrise: built a Java/Spark machine-learning pipeline for communications during a short specialist engagement.
- ThirdEye: developed a Kubernetes/TensorFlow pipeline for video recognition during a short specialist engagement.
- Therapy Box: developed a deep-learning speech-classification prototype for research into learning difficulties in children's speech.
IT/IS Executive | Microsharp Corporation | February 2003–May 2007
Designed and tested company IT and communications infrastructure with automation and redundancy.
Technical foundation
Platform engineering: Rust, Python, TypeScript, APIs, distributed systems, PostgreSQL, Redis, containers, CI/CD and Cloudflare Workers. Historical delivery also includes Java/Spark, TensorFlow, PyTorch and MATLAB.
Applied AI and systems: machine learning, NLP, knowledge graphs, graph embeddings, search, image processing, sensor fusion, privacy-preserving approaches and synthetic data. Systems engineering, technical architecture, prototyping and delivery governance connect the research and implementation work.
Patents
Secure distributed data storage | UK application GB1815423.7
Granted patent GB2574076B. Led the prototyping team and supported patenting; sponsored and mentored the named inventor.
Loyalty-program technology | Patent application WO2013057491
Named co-inventor of “Method and system for providing a loyalty program” for Shopitize.
Awards
- Platinum Prize, Build on Redis Hackathon, 2021: The Pattern, combining NLP-based document exploration, graph-based search and spatial VR/AR exploration.
- Best Student Paper Award, ISSPA, 2007: “Emitter Geolocation using fusion of TDOA Data with a Particle Filter”, co-authored with R. F. Ormondroyd.
- Golden Award, Codility, January 2016: Calcium2015 programming challenge.
- Winner, Nationwide internal IT Architecture Raspberry Pi challenge, 2017: machine-learning prototype for detecting data-compliance issues.
Selected talks
- CNCF, 23 March 2023: co-speaker, “Real-time pipeline monitoring for the energy sector”.
- Oxford Praxis Forum, Green Templeton College, University of Oxford, 28 November 2022: speaker, “What next after Google?”.
- EuroPython, 28 July 2021: co-presenter with Dvir Dukhan, “The Pattern: Machine Learning Natural Language Processing meets VR/AR”.
- RedisConf, April 2021: “Using the Redis ecosystem to build NLP-based services”; selected in Redis’s staff-voted conference highlights for “Best I-Can’t-Believe-You’re-Using-Redis-For-THAT Story”.
Competition projects and publication
MedGemma competition, February 2026: built a solo knowledge-grounded research prototype in Rust, including entity extraction, a knowledge graph, multi-agent orchestration, MedGemma integration and an evaluation harness; evaluated across 18 scripted cases with CPU/GPU inference.
Industrial publication: Co-author, Monitoring and Anomaly Detection Approaches with AI and Data Analytics for Pipelines, Pipeline Technology Journal, 2023.
Education
PhD | Cranfield University
Thesis: Image processing and agent-based framework for the geolocation of emitters (2010).
MSc | Bauman Moscow State Technical University
Computer Science and Computer Engineering.
TOGAF 9 Certified | The Open Group
Career themes
The following connections are thematic interpretations of the experience above. They do not assert additional inventions or SFIA ratings.
Digital twins and industrial AI
At Cranfield, researched in-vehicle sensor networks within a Boeing- and Rolls-Royce-backed Integrated Vehicle Health Management (IVHM) programme. This work connects to the sensor-data and modelling foundations of digital twins; later Klarian work applied physical models and machine learning to industrial monitoring.
Graph-based knowledge and grounded AI
The Pattern combined document exploration and graph-based search. Terraphim develops role-specific knowledge graphs and interpretable graph embeddings; the MedGemma research prototype brings knowledge graphs into a grounded AI workflow.
Privacy and enterprise AI capability
At Nationwide, led the Synthetic Data Platform from inception to enterprise capability and developed privacy-preserving machine-learning approaches. Terraphim continues the concern for data control through local-first search and context engineering.
From prototypes to autonomous delivery
Built prototyping capability and architecture-as-code demonstrations at Nationwide, then developed reusable delivery practices and an autonomous delivery factory connecting agent execution with separate review, verification and validation gates.
SFIA skills and behaviour signals
An evidence-informed mapping to SFIA 9, not certification or a formal responsibility assessment. Levels are provisional; upper candidates require further evidence. Behaviour signals are ungraded. Awards, talks and job titles do not independently establish a responsibility level.
Each capability below retains its context, rationale and evidence gaps. Data vocabulary explains the mapping fields.
Profile · profile-01
Managing Partner, CTO and Head of AI combining technology direction with hands-on engineering in Rust, Python and TypeScript. Builds AI capability and the software platforms that put it to work: industrial monitoring and anomaly detection, knowledge search, client applications and autonomous engineering infrastructure. Experience spans startup team building, enterprise architecture at Nationwide and applied research at Cranfield. Leads delivery while remaining close to the code, data and engineering decisions.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Programming/software development PROG | 5 | — |
| Machine learning MLNG | 5 | — |
Behaviour signals
- Leadership: Links direction to delivery
- Digital mindset: Combines domain expertise and current implementation tools
Mapping rationale: Summary supported by the detailed platform, banking and industrial claims; it introduces no additional level evidence.
Evidence category: mixed_summary; mapping confidence: medium.
Evidence gaps: Assess the detailed rows; do not count this summary again.
Zestic AI · zestic-ai-02
- Set technology direction and delivery approach across the client portfolio, combining hands-on platform engineering with reusable AI infrastructure and staged engineering reviews.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Systems development management DLMG | 5 | — |
| Systems and software lifecycle engineering SLEN | 5 | 6 |
Behaviour signals
- Leadership: Sets delivery approach across projects
- Planning: Stages reviews and reusable infrastructure
Mapping rationale: Portfolio delivery direction and staged engineering reviews fit development leadership and lifecycle practice ownership.
Evidence category: first_party_operating_records; mapping confidence: medium.
Evidence gaps: For SLEN6: confirm organisational commitment, cross-lifecycle policies, risk ownership and evaluation of the working environment; DLMG6 additionally needs technical/financial/quality targets and resource authority.
Zestic AI · zestic-ai-03
- Shape a services-and-platform strategy that turns client delivery into reusable capabilities; connect architecture choices, pilot scope and delivery priorities to the wider business model.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Enterprise and business architecture STPL | 5 | — |
| Strategic planning ITSP | 5 | — |
Behaviour signals
- Decision-making: Connects pilot scope to business priorities
- Planning: Balances services and reusable platform priorities
Mapping rationale: Business-model canvas connects services/platform choices to capability and delivery priorities; ITSP is a candidate only insofar as this informs the whole business strategy.
Evidence category: first_party_operating_records; mapping confidence: medium.
Evidence gaps: Show approved strategy, authority, investment decisions and outcome reviews before claiming enterprise strategy ownership or level6.
Zestic AI · zestic-ai-04
- Confidential digital-learning provider: lead architecture and engineering across assessment services, web applications and data models; implement assessment evidence handling, learner-journey testing, analytics instrumentation and CI/CD.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Programming/software development PROG | 5 | — |
| Systems and software lifecycle engineering SLEN | 4 | — |
Behaviour signals
- Leadership: Leads client architecture and engineering
- Problem-solving: Connects assessment, application and data concerns
Mapping rationale: Architecture ownership and implementation across a multi-component client platform support significant solution/software responsibility; CI/CD is narrower lifecycle implementation.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Add acceptance evidence, delivery accountability and specific review decisions; records include integration work, not blanket production completion.
Zestic AI · zestic-ai-05
- Confidential impact-finance lender: engineer borrower workflows, financial assessment, document generation and CRM integration, bringing operational lending processes into a connected software platform.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 4 | — |
| Software design SWDN | 4 | — |
Behaviour signals
- Problem-solving: Integrates lending workflows and documents
- Digital mindset: Applies software to operational processes
Mapping rationale: Connected borrower workflows, assessment, generated documents and CRM demonstrate complex application/integration construction.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Confirm end-to-end technical accountability before level5; no AI underwriting or regulatory certification inferred.
Zestic AI · zestic-ai-06
- Build TruthForge, a Rust-based narrative-analysis application, with supporting deployment and website delivery.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 4 | — |
Behaviour signals
- Digital mindset: Implements a Rust application and delivery tooling
Mapping rationale: Rust narrative application and delivery contribution evidence software construction, not ownership of every lifecycle stage.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Provide reviewed contributions, lifecycle ownership and release acceptance for level5.
Terraphim AI · terraphim-ai-02
- Lead development of a Rust/WebAssembly knowledge-search platform combining role-specific knowledge graphs, deterministic text matching, command-line tools and Model Context Protocol integration.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 5 | — |
| Solution architecture ARCH | 5 | — |
Behaviour signals
- Leadership: Leads platform development
- Decision-making: Combines graph and deterministic retrieval components
Mapping rationale: Leadership plus implemented Rust/WASM, CLI and MCP components supports technical platform responsibility and integration architecture.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Document collaborator responsibilities, roadmap trade-offs and operating outcomes; public source does not prove organisation-wide policy authority.
Terraphim AI · terraphim-ai-03
- Build local-first, privacy-preserving search and context-engineering capabilities that connect domain knowledge, session history and reusable learning to AI tools.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Software design SWDN | 5 | — |
| Knowledge management KNOW | 4 | — |
Behaviour signals
- Security, privacy and ethics: Chooses local-first privacy-conscious retrieval
- Improvement mindset: Reuses context and accumulated learning
Mapping rationale: Context search design supports software design; organising domain/session knowledge supports a knowledge initiative, not proof of an organisation-wide knowledge programme.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Show knowledge adoption/impact and policies for KNOW5; privacy architecture is not a full ethics or security assurance programme.
Terraphim AI · terraphim-ai-04
- Develop Terraphim’s distinctive graph-embedding approach in Rust, using curated concepts, synonym mappings and graph co-occurrence to rank documents and expose the relationships behind search results.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 5 | — |
| Software design SWDN | 5 | — |
Behaviour signals
- Creativity: Applies distinctive graph ranking design
- Problem-solving: Makes concept relationships inspectable
Mapping rationale: Inspected Rust graph code ranks documents using concepts, synonyms and co-occurrence; this is concrete software/design evidence, not by itself learned-model MLNG evidence.
Evidence category: first_party_inspected_source; mapping confidence: medium.
Evidence gaps: Add design decisions and comparative evaluation; no uniqueness, patent or performance superiority level inference.
Autonomous delivery factory · autonomous-delivery-factory-02
- Built and operate the AI Dark Factory, connecting issue prioritisation and agent execution with separate review, verification and validation gates across development projects.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Systems and software lifecycle engineering SLEN | 5 | 6 |
| Systems development management DLMG | 5 | — |
Behaviour signals
- Planning: Connects prioritisation, execution and assurance
- Improvement mindset: Builds repeatable delivery practices
- Leadership: Sets separate review responsibilities
Mapping rationale: Implemented operating pipeline joins issue workflow with independent stages of engineering assurance.
Evidence category: first_party_deployment_records; mapping confidence: medium.
Evidence gaps: For SLEN6 confirm organisational commitment and integration of lifecycle policy, risk management and evaluation; deployed cross-project working environment is a candidate6 signal. DLMG stays5 pending resource/target evidence; no productivity gains inferred.
Autonomous delivery factory · autonomous-delivery-factory-03
- Extended AgentForge, a Gitea-based development platform, with scoped agent identity, signed audit events, a unified event stream and branch-based collaboration.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 5 | — |
| Software design SWDN | 5 | — |
Behaviour signals
- Security, privacy and ethics: Implements identity scope and signed auditability
- Collaboration: Enables branch and event collaboration
Mapping rationale: Implemented AgentForge identity, audit and event extensions demonstrate complex platform changes and design controls.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Security mechanisms do not establish SCTY governance or independent security assurance; confirm contribution scope and lifecycle accountability.
Autonomous delivery factory · autonomous-delivery-factory-04
- Connected the development platform to a kanban workflow through a Rust bridge, making task coordination and review part of the engineering system.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 4 | — |
| Systems and software lifecycle engineering SLEN | 4 | — |
Behaviour signals
- Improvement mindset: Integrates task coordination and reviews
- Digital mindset: Automates kanban hand-offs
Mapping rationale: Rust bridge integrates task and review workflow with the development environment.
Evidence category: first_party_implementation_records; mapping confidence: medium.
Evidence gaps: Show team-wide adoption and ownership of standards before higher lifecycle level.
Klarian · klarian-02
- Led AI and technology development for industrial pipeline monitoring and analytics, combining engineering models and machine learning to investigate pump efficiency and equipment anomalies for UK pipeline operators.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Machine learning MLNG | 5 | — |
| Scientific modelling SCMO | 5 | — |
Behaviour signals
- Leadership: Directs applied industrial AI
- Problem-solving: Combines engineering behaviour and ML
Mapping rationale: Industrial cases support mixed physics/model work; personal direction is user-reported and publication confirms co-authorship and CTO context.
Evidence category: mixed_publisher_and_first_party; mapping confidence: medium.
Evidence gaps: Confirm personal model decisions, MLOps scope, team/budget and deployed operational outcomes; company work is not all personally authored.
Klarian · klarian-03
- Co-authored published client case studies on pump-performance analysis and orifice-plate anomaly detection, connecting sensor data and physical behaviour to operational decisions.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Data science DATS | 4 | — |
| Scientific modelling SCMO | 4 | — |
Behaviour signals
- Communication: Explains client case findings
- Collaboration: Co-authors industrial analyses
Mapping rationale: Published pump/orifice studies connect sensor analysis and physical interpretation to decisions, with joint attribution.
Evidence category: publisher_corroborated; mapping confidence: medium.
Evidence gaps: Author contribution record and project leadership needed for level5; do not count publication as sole project ownership.
Klarian · klarian-04
- Co-authored analysis identifying potential energy-cost reduction of approximately £5,000 per month per average mainline pump in the examined network section, conditional on restoring efficiency to as-new levels.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Data analytics DAAN | 4 | — |
Behaviour signals
- Problem-solving: Quantifies conditional improvement opportunity
- Communication: States scope and restoration condition
Mapping rationale: Published conditional energy-cost estimate is analysis communication, not realised benefits management.
Evidence category: publisher_corroborated_estimate; mapping confidence: medium.
Evidence gaps: Validate personal analytical contribution and measurement basis; never treat estimated £5,000/month as realised savings.
Nationwide Building Society · nationwide-03
- Established and ran a prototyping engineering lab; developed distributed-infrastructure prototypes and contributed to AI and blockchain strategy. Sponsored and mentored the team member whose work resulted in a distributed-data-storage patent.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Innovation management INOV | 5 | 6 |
Behaviour signals
- Leadership: Establishes lab and supports team invention
- Learning and development: Sponsors and mentors an inventor
- Creativity: Creates a prototyping environment
Mapping rationale: Lab establishment and operation support an innovation capability; level6 is a tentative organisational-capability signal, not a global career grade.
Evidence category: mixed_institution_and_first_party; mapping confidence: medium.
Evidence gaps: Confirm organisational commitment, decision rights and innovation outcomes for6; inventor is another person.
Nationwide Building Society · nationwide-04
- Led teams of 2–12 technical architects across parallel programmes including mobile banking, Open Banking, strategic origination, information management and regulatory change.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Systems development management DLMG | 5 | — |
Behaviour signals
- Leadership: Leads 2–12 architects
- Collaboration: Coordinates parallel programme domains
Mapping rationale: Reported architectural team span and parallel banking programmes support delivery/architecture leadership at significant work scope.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Headcount alone does not establish6; obtain mandate, delegation/resource evidence and delivery results.
Nationwide Building Society · nationwide-05
- Set technical quality assurance and governance for supplier designs and build deliverables, including IBM and Accenture; aligned project architecture with enterprise strategy and challenged technical debt.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Quality assurance QUAS | 5 | — |
| Solution architecture ARCH | 5 | 6 |
Behaviour signals
- Decision-making: Challenges supplier designs and technical debt
- Collaboration: Aligns suppliers with enterprise direction
Mapping rationale: Supplier design/build assurance fits complex-domain quality reviews and architectural conformance.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Combined with nationwide04, cross-programme governance is a candidate ARCH6 signal. Confirm target architecture ownership and functional/service/cost trade-off decisions; obtain assurance findings and organisational remit before QUAS6. No board-level GOVN inferred.
Nationwide Building Society · nationwide-06
- Led the Synthetic Data Platform from inception to enterprise capability to support data-driven innovation and reduce reliance on live data.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Systems development management DLMG | 5 | — |
Behaviour signals
- Leadership: Takes platform from inception to capability
- Security, privacy and ethics: Seeks less reliance on live data
Mapping rationale: Historical partner CV reports end-to-end platform leadership; privacy purpose supports a behaviour signal, not guaranteed privacy outcomes.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Supply delivery/adoption records, remit and outcome evidence; do not infer measured live-data reduction or ML modelling ownership.
Nationwide Building Society · nationwide-07
- Developed privacy-preserving machine-learning and digital-twin approaches.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Machine learning MLNG | 4 | — |
| Scientific modelling SCMO | 4 | — |
Behaviour signals
- Security, privacy and ethics: Explores privacy-preserving methods
- Creativity: Combines ML and digital-twin approaches
Mapping rationale: Reported privacy ML/twin development aligns to applied modelling; sparse description limits assessment confidence.
Evidence category: historical_cv_report; mapping confidence: low.
Evidence gaps: Provide artifacts, exact role, evaluated methods and outputs; capability topics alone cannot establish5.
Nationwide Building Society · nationwide-08
- Shaped an enterprise DevOps blueprint and demonstrated architecture as code through a proof of concept; proposed a common business-value measure for change delivery.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Enterprise and business architecture STPL | 5 | — |
| Systems and software lifecycle engineering SLEN | 5 | — |
Behaviour signals
- Improvement mindset: Proposes DevOps and value measurement
- Digital mindset: Demonstrates architecture as code
Mapping rationale: Enterprise blueprint and proof of concept support architecture/lifecycle practice design, with adoption explicitly unproven.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Confirm approved adoption, decision ownership and measured effectiveness before6; proposed metric is not an operated measurement capability.
M2M Ecosphere · m2m-ecosphere-02
- Established innovation processes, an R&D ecosystem, knowledge management and organisation design to support innovation across Group Olvia.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Innovation management INOV | 5 | 6 |
| Knowledge management KNOW | 5 | — |
| Organisation design and implementation ORDI | 5 | — |
Behaviour signals
- Leadership: Establishes group innovation capability
- Improvement mindset: Creates repeatable R&D practices
- Collaboration: Builds an R&D ecosystem
Mapping rationale: Reported establishment of innovation processes, knowledge management and organisation design spans capability creation.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Historical CV only: seek approved designs, organisational commitment, adoption, budget/quality accountability and impact;6 is tentative INOV signal.
M2M Ecosphere · m2m-ecosphere-03
- Deployed an AI prototype for the Group’s copper supply chain from the Democratic Republic of the Congo to China, addressing route optimisation and response to unexpected disruption.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Software design SWDN | 4 | — |
| Machine learning MLNG | Ungraded | — |
Behaviour signals
- Problem-solving: Addresses routing and disruption
- Creativity: Applies AI prototype to supply-chain problem
Mapping rationale: Reported deployed AI prototype supports software design; AI does not necessarily mean learned models, so MLNG remains a relevant area without a level.
Evidence category: historical_cv_report; mapping confidence: low.
Evidence gaps: Confirm algorithms and personal work; prototype is not evidence of production optimisation or realised savings.
Shopitize · shopitize-02
- Served as the most senior technology professional, aligning business strategy, product development and platform architecture. Managed up to 32 engineers and designers across four locations, then hired and built an in-house team.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Systems development management DLMG | 5 | 6 |
| Organisation design and implementation ORDI | 5 | — |
Behaviour signals
- Leadership: Leads distributed technology teams
- Collaboration: Aligns business and product work
- Adaptability: Transitions to an in-house team
Mapping rationale: Technical ownership, four-location team management and in-house transition support architecture, development and team-structure responsibilities.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: For DLMG6 the reported technology/resource remit is a candidate signal; confirm development policy, security/privacy ownership and technical/financial/quality targets. No HR-specialist RESC inferred merely from hiring.
Shopitize · shopitize-03
- Designed and oversaw automated receipt processing using image processing and OCR; owned architecture spanning data processing, backend services, mobile APIs and a mix of public/private cloud infrastructure.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Solution architecture ARCH | 5 | — |
| Software design SWDN | 5 | — |
Behaviour signals
- Decision-making: Selects architecture across processing and infrastructure
- Leadership: Oversees receipt-processing delivery
Mapping rationale: Reported ownership spans OCR processing, backend, mobile APIs and hybrid infrastructure.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Obtain architecture artifacts and decision/result evidence; do not infer ML model development from OCR alone.
Shopitize · shopitize-04
- Designed REST APIs, publish/subscribe processing and payment integrations with BACS and PayPal; built a product-matching search engine to replace Solr.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 4 | — |
| Software design SWDN | 4 | — |
Behaviour signals
- Problem-solving: Integrates APIs, payments and search
- Improvement mindset: Replaces product matching component
Mapping rationale: Implementation across APIs, asynchronous processing and product search supports complex software design/construction.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Show ownership across lifecycle to substantiate5; no payment compliance mandate inferred.
Shopitize · shopitize-05
- Mentored data scientists and Science to Data Science participants.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Professional development PDSV | Ungraded | — |
Behaviour signals
- Learning and development: Mentors data scientists
- Leadership: Supports participants development
Mapping rationale: Mentoring is direct behavioural evidence; PDSV is relevant but structured development planning and review are unshown.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Provide development plans, needs analysis and progression outcomes before assigning PDSV4 or5.
Cranfield University · cranfield-02
- Developed wavelet-packet methods for mobile spectrum sharing and implemented the waveform on Anritsu vector signal generation and analysis hardware.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Radio frequency engineering RFEN | 4 | — |
| Formal research RSCH | 4 | — |
Behaviour signals
- Problem-solving: Implements and tests waveform methods
- Creativity: Develops spectrum-sharing methods
Mapping rationale: Waveform research plus instrument implementation supports radio engineering and research execution.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Confirm personal research planning, tests and external funding role before RSCH5; employment title alone gives no level.
Cranfield University · cranfield-03
- Researched communications for in-vehicle sensor networks in an aerospace health-monitoring programme supported by an industry consortium led by Rolls-Royce and Boeing.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Formal research RSCH | 4 | — |
| Radio frequency engineering RFEN | 4 | — |
Behaviour signals
- Collaboration: Works in consortium research context
- Problem-solving: Investigates vehicle sensor communications
Mapping rationale: Sensor-communications research supports domain research/RF skill signals; consortium membership is context only.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Provide exact deliverables and contribution records; consortium names do not imply personal customer influence or full system delivery.
Cranfield University · cranfield-04
- Developed image-processing and data-fusion methods for emitter geolocation, comparing Hough-transform approaches with particle filtering in doctoral research.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Formal research RSCH | 4 | — |
| Scientific modelling SCMO | 4 | — |
Behaviour signals
- Problem-solving: Compares alternative geolocation methods
- Creativity: Combines image processing and data fusion
Mapping rationale: Institutionally deposited doctoral work supports substantive research methods, model comparison and dissemination.
Evidence category: mixed_thesis_and_first_party; mapping confidence: medium.
Evidence gaps: Confirm goal/funding authority for RSCH5; qualification and publication alone do not establish senior research management.
Selected applied AI engagements · specialist-engagements-01
- Memrise: built a Java/Spark machine-learning pipeline for communications during a short specialist engagement.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Data engineering DENG | 4 | — |
| Machine learning MLNG | 4 | — |
Behaviour signals
- Problem-solving: Builds communications pipeline
Mapping rationale: Reported Java/Spark ML pipeline supports pipeline and ML implementation; exact operating scope is unknown.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Request source, test outcomes, personal scope and production status; no leadership level5 inferred.
Selected applied AI engagements · specialist-engagements-02
- ThirdEye: developed a Kubernetes/TensorFlow pipeline for video recognition during a short specialist engagement.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Data engineering DENG | 4 | — |
| Machine learning MLNG | 4 | — |
Behaviour signals
- Digital mindset: Combines orchestration with video models
Mapping rationale: Reported Kubernetes/TensorFlow video pipeline supports ML/data engineering implementation.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Request architecture/model evaluation and operating evidence; no accuracy or production claim.
Selected applied AI engagements · specialist-engagements-03
- Therapy Box: developed a deep-learning speech-classification prototype for research into learning difficulties in children's speech.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Machine learning MLNG | 4 | — |
Behaviour signals
- Problem-solving: Builds speech-classification research prototype
Mapping rationale: Research speech prototype supports model development within a bounded exploratory task.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Provide model/evaluation artifacts and responsibility scope; no clinical validation or diagnostic capability inferred.
Microsharp Corporation · microsharp-01
IT/IS Executive | Microsharp Corporation | February 2003–May 2007
Designed and tested company IT and communications infrastructure with automation and redundancy.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Infrastructure design IFDN | 4 | — |
Behaviour signals
- Problem-solving: Designs redundancy and automation
- Digital mindset: Uses automation in infrastructure
Mapping rationale: Design and testing of company IT/communications supports infrastructure engineering.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Confirm scale, requirements, test results and design authority before5.
Technical foundation · technical-foundation-01
Platform engineering: Rust, Python, TypeScript, APIs, distributed systems, PostgreSQL, Redis, containers, CI/CD and Cloudflare Workers. Historical delivery also includes Java/Spark, TensorFlow, PyTorch and MATLAB.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | Ungraded | — |
| Data engineering DENG | Ungraded | — |
| Systems and software lifecycle engineering SLEN | Ungraded | — |
Behaviour signals
- Digital mindset: Shows breadth of applied tools
Mapping rationale: Technology index points to concrete project rows; tool names alone are not SFIA competence or current proficiency evidence.
Evidence category: mixed_summary; mapping confidence: medium.
Evidence gaps: Use underlying project evidence; do not assign uniform levels to every listed language/tool.
Technical foundation · technical-foundation-02
Applied AI and systems: machine learning, NLP, knowledge graphs, graph embeddings, search, image processing, sensor fusion, privacy-preserving approaches and synthetic data. Systems engineering, technical architecture, prototyping and delivery governance connect the research and implementation work.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Machine learning MLNG | Ungraded | — |
| Scientific modelling SCMO | Ungraded | — |
| Solution architecture ARCH | Ungraded | — |
Behaviour signals
- Problem-solving: Bridges modelling and systems delivery
Mapping rationale: Capability index summarises heterogeneous projects; graph embedding can be deterministic software and need not be MLNG.
Evidence category: mixed_summary; mapping confidence: medium.
Evidence gaps: Assess each project; no uniform level across all methods or recency implied.
Patents · patents-01
Secure distributed data storage | UK application GB1815423.7
Granted patent GB2574076B. Led the prototyping team and supported patenting; sponsored and mentored the named inventor.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Innovation management INOV | 5 | — |
Behaviour signals
- Leadership: Sponsors team invention
- Learning and development: Mentors named inventor
Mapping rationale: Team leadership and patenting support corroborate the innovation-lab story; no Alexander inventorship claimed.
Evidence category: mixed_patent_institution_first_party; mapping confidence: medium.
Evidence gaps: Duplicate support for nationwide03, not independent skill achievement; patent grant verifies legal publication, not leadership level.
Patents · patents-02
Loyalty-program technology | Patent application WO2013057491
Named co-inventor of “Method and system for providing a loyalty program” for Shopitize.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Software design SWDN | Ungraded | — |
Behaviour signals
- Creativity: Named co-inventor of a technology application
Mapping rationale: Published co-inventorship is concrete creative contribution; it does not establish implementation, work autonomy or management scope.
Evidence category: patent_record_corroborated; mapping confidence: medium.
Evidence gaps: Need contribution/design artifacts and responsibility evidence to assign a software-design level.
Awards · awards-01
- Platinum Prize, Build on Redis Hackathon, 2021: The Pattern, combining NLP-based document exploration, graph-based search and spatial VR/AR exploration.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | Ungraded | — |
| Software design SWDN | Ungraded | — |
Behaviour signals
- Creativity: Combines NLP, graph and spatial exploration
Mapping rationale: Organiser corroborates prize and project; recognition supports creativity without defining workplace accountability.
Evidence category: organiser_corroborated; mapping confidence: medium.
Evidence gaps: Assess implementation artifacts and contribution scope for any skill level.
Awards · awards-02
- Best Student Paper Award, ISSPA, 2007: “Emitter Geolocation using fusion of TDOA Data with a Particle Filter”, co-authored with R. F. Ormondroyd.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Formal research RSCH | Ungraded | — |
| Scientific modelling SCMO | Ungraded | — |
Behaviour signals
- Creativity: Recognised geolocation research
- Communication: Co-authored research paper
Mapping rationale: Thesis reports student-paper award and co-authored modelling research; organiser certificate not inspected.
Evidence category: institutional_first_party_record; mapping confidence: medium.
Evidence gaps: Award supports research evidence, not a responsibility grade; see cranfield04.
Awards · awards-03
- Golden Award, Codility, January 2016: Calcium2015 programming challenge.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | Ungraded | — |
Behaviour signals
- Problem-solving: Programming-challenge recognition
Mapping rationale: Personal record reports Codility recognition; no independently inspected result or workplace delivery context.
Evidence category: first_party_personal_record; mapping confidence: low.
Evidence gaps: Obtain result/certificate and solution; do not infer production proficiency or percentile.
Awards · awards-04
- Winner, Nationwide internal IT Architecture Raspberry Pi challenge, 2017: machine-learning prototype for detecting data-compliance issues.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Machine learning MLNG | Ungraded | — |
Behaviour signals
- Creativity: Applies ML to a compliance prototype
- Problem-solving: Explores data-compliance detection
Mapping rationale: Historical CVs report internal win and prototype; competition result does not establish deployed compliance capability.
Evidence category: historical_cv_report; mapping confidence: medium.
Evidence gaps: Obtain organiser/result and prototype evidence; no compliance assurance or savings inference.
Selected talks · talks-01
- CNCF, 23 March 2023: co-speaker, “Real-time pipeline monitoring for the energy sector”.
Behaviour signals
- Communication: Presents specialist concepts to external audiences
- Collaboration: Shares presentation with co-speakers
Mapping rationale: Official event evidence supports speaking/topic attribution; audience engagement and communication impact have not been evaluated.
Evidence category: organiser_event_record; mapping confidence: medium.
Evidence gaps: No professional skill or full behavioural level from a title/listing alone; inspect recording, feedback and decision impact.
Selected talks · talks-02
- Oxford Praxis Forum, Green Templeton College, University of Oxford, 28 November 2022: speaker, “What next after Google?”.
Behaviour signals
- Communication: Presents specialist concepts to external audiences
Mapping rationale: Official event evidence supports speaking/topic attribution; audience engagement and communication impact have not been evaluated.
Evidence category: institution_event_listing; mapping confidence: medium.
Evidence gaps: No professional skill or full behavioural level from a title/listing alone; inspect recording, feedback and decision impact.
Selected talks · talks-03
- EuroPython, 28 July 2021: co-presenter with Dvir Dukhan, “The Pattern: Machine Learning Natural Language Processing meets VR/AR”.
Behaviour signals
- Communication: Presents specialist concepts to external audiences
- Collaboration: Shares presentation with co-speakers
Mapping rationale: Official event evidence supports speaking/topic attribution; audience engagement and communication impact have not been evaluated.
Evidence category: organiser_event_record; mapping confidence: medium.
Evidence gaps: No professional skill or full behavioural level from a title/listing alone; inspect recording, feedback and decision impact.
Selected talks · talks-04
- RedisConf, April 2021: “Using the Redis ecosystem to build NLP-based services”; selected in Redis’s staff-voted conference highlights for “Best I-Can’t-Believe-You’re-Using-Redis-For-THAT Story”.
Behaviour signals
- Communication: Presents specialist concepts to external audiences
Mapping rationale: Official event evidence supports speaking/topic attribution; audience engagement and communication impact have not been evaluated.
Evidence category: organiser_event_record; mapping confidence: medium.
Evidence gaps: No professional skill or full behavioural level from a title/listing alone; inspect recording, feedback and decision impact.
Competition projects · competition-projects-01
MedGemma competition, February 2026: built a solo knowledge-grounded research prototype in Rust, including entity extraction, a knowledge graph, multi-agent orchestration, MedGemma integration and an evaluation harness; evaluated across 18 scripted cases with CPU/GPU inference.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Programming/software development PROG | 5 | — |
| Machine learning MLNG | 4 | — |
Behaviour signals
- Creativity: Builds knowledge-grounded prototype
- Problem-solving: Constructs evaluation cases
- Digital mindset: Integrates CPU/GPU inference
Mapping rationale: Solo Rust prototype and recorded 18-case harness support full prototype software responsibility and model integration, not clinical validation.
Evidence category: first_party_source_and_recorded_evaluation; mapping confidence: medium.
Evidence gaps: Recorded test evidence not rerun; integration is not proof of training novel models or production MLOps; no clinical outcome or award.
Industrial publication · publication-01
Industrial publication: Co-author, Monitoring and Anomaly Detection Approaches with AI and Data Analytics for Pipelines, Pipeline Technology Journal, 2023.
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Data science DATS | Ungraded | — |
Behaviour signals
- Communication: Co-authors industrial technical publication
- Collaboration: Shares authorship
Mapping rationale: Publisher supports authorship and topic; underlying analytical activity is assessed in Klarian rows.
Evidence category: publisher_corroborated; mapping confidence: medium.
Evidence gaps: Do not double-count; publication alone is insufficient for DATS level.
Education · education-01
PhD | Cranfield University
Thesis: Image processing and agent-based framework for the geolocation of emitters (2010).
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Formal research RSCH | Ungraded | — |
Behaviour signals
- Learning and development: Completes doctoral research
Mapping rationale: Institutional thesis supports research qualification and topic, not workplace responsibility level.
Evidence category: institution_corroborated; mapping confidence: medium.
Evidence gaps: Use cranfield04 research activity for indicative mapping; doctorate does not imply SFIA6/7.
Education · education-02
MSc | Bauman Moscow State Technical University
Computer Science and Computer Engineering.
Behaviour signals
- Learning and development: Postgraduate technical education
Mapping rationale: University speaker biography corroborates degree subject; diploma not inspected.
Evidence category: institution_biography_corroborated; mapping confidence: medium.
Evidence gaps: No responsibility level from qualification.
Education · education-03
TOGAF 9 Certified | The Open Group
| SFIA skill | Provisional level | Upper candidate |
|---|---|---|
| Enterprise and business architecture STPL | Ungraded | — |
| Solution architecture ARCH | Ungraded | — |
Behaviour signals
- Learning and development: Architecture framework credential
Mapping rationale: First-party TOGAF credential listing suggests framework knowledge; registry and issue details unverified.
Evidence category: first_party_credential_record; mapping confidence: medium.
Evidence gaps: TOGAF certification is not SFIA certification; assess applied architecture rows and verify registry if needed.
Updated 12 September 2026. SFIA is owned by the SFIA Foundation.