Rob Gilks
AI platform engineer & technical lead. More than 30 years of shipping systems that get used, most recently the AI auto-marking APIs behind Cambridge English exams.
profile
I make AI systems work in production. At Cambridge University Press & Assessment I led the team behind the Text and Speech APIs that mark written and spoken English exams - platform engineering for machine learning, keeping other teams' models dependable in front of real exam candidates at 99.98% uptime over twelve months.
On my own products I do the model side as well: fine-tuning, calibration, frozen benchmarks, and the quality gates that decide whether generated content is fit to publish. Between the two I have done every part of an AI product, from the model down to the infrastructure it runs on.
I lead by working alongside people rather than above them; four to six engineers is where I do my best work. My production work is documentation-first - architecture docs, decision records and diagrams kept as code. I direct AI coding agents the same way I direct a team: decompose the work, verify the output, stay accountable for the result. There's a whole page on how I lead a technical team.
Available immediately, working remotely from London.
skills
A working stack across product, platform and applied ML - not a single specialism.
platform & MLOps
languages
AI & ML
AI evaluation
web & cloud
practice
personal projects
Browser games and GPU simulations - not production experience.
experience
- Led the TAPI team within Assessment & Research Capabilities (ARC), building and operating the Text and Speech APIs that power automated marking across Cambridge English assessments, including Write & Improve and Speak & Improve - and stayed the largest individual contributor to the Text API while leading it. The platform held 99.98% uptime across twelve months at around a 1% change-failure rate.
- Operated the response-validity suite that decides whether a submission can be marked at all - off-topic, gibberish, insufficient-English, offensive and repetition detection, plus copy-detection. The detectors were the ML teams' work; running them dependably in production was mine.
- Instigated and built two of the platform's systems: Text Orchestrator, the Clojure/AWS service that drives automated text assessment, and the multi-account automarking administration dashboard in Next.js, React and TypeScript on AWS Amplify/Cognito.
- Productionised Python NLP/ML inference as Dockerised SQS workers with CI/CD, diagnostics, log redaction and LocalStack/AWS tests, hardened the services around them with retries, idempotency and alarms, and was one of the most active contributors to the Terraform monorepo underneath.
- Owned my team's part of the Cyber Essentials Plus compliance programme - the assessment APIs - covering AWS account scope reduction, CVE and IAM remediation, secrets, backup and disaster recovery, and a written security incident response procedure.
- Moved the team towards continuous delivery - deployment separated from release behind feature flags, four environments gated by automated acceptance tests, and DORA metrics tracked with a tool I wrote and open-sourced. Ran an Accelerate reading group to build the case.
- Built the team's documentation culture - architecture and diagrams as code, RFCs, runbooks, incident response and a public API documentation site - and introduced its AI-assisted code review process, with automated pre-review on every pull request and risk-tiered approvals so human review concentrates on intent and operational impact.
My own company since 1998; full-time on it since leaving Cambridge.
- Building and running live AI products for language learning and assessment (below) on Cloudflare Workers, with serverless GPU inference on Modal.
- Fine-tuning and deploying transformer models on serverless GPUs, with post-hoc calibration and grammar correction alongside the scorer.
- Choose and gate models on measured evidence: frozen benchmarks with held-out folds, fail-closed quality checks on generated content, and side-by-side model comparisons scored on task success, convergence and failure modes.
- Build throwaway projects to learn things properly. The product is often trivial; the point is having a real reason to work out how something behaves.
- Senior engineer on Speak & Improve - an AI feedback tool that helps English learners worldwide improve their speaking, built in Clojure around custom marking models.
- Moved to the API team, which became TAPI within ARC following ELiT's integration into Cambridge in 2025.
- Led four to six engineers while remaining hands-on and owning delivery for a system that enabled clinical research sites to communicate with trial patients by SMS.
- The role ended when OneStudyTeam cut 40% of its staff in a single day, citing changes in market conditions.
Concurrent contract roles for multiple clients through my company.
- CTO, Limpid Markets (2018-2020) - set the technical direction for a precious-metals derivatives trading platform and transitioned software delivery from a London team to a Ukraine-based team.
- Technical Lead, Predira (2020-2022) - built the early prototypes of a customisable brokerage trading platform with the founder, then grew the team and engineering practices.
- Technical Lead, SimPlay (2019-2021) - rebuilt a platform for running collaborative role-play training experiences online.
- Ten years building interactive products and campaigns for Nike, Heineken, Nissan, the BBC and Ford - including Heineken Star Player (Cannes Gold Cyber Lion) and the BBC's Story of Life.
- LTA - TotalTennis club website platform and live tournament scoring (Nottingham, Eastbourne). Oxford University LTG - Java learning tool middleware. Play Sport / Pretzel Logic - sports club and government web systems. Ampac Australia - fire-detection systems, from electronics technician to R&D engineer.
selected projects
Built and run independently. All live and open where noted - the code is there to read.
comprehendo
Reading practice in 15 languages. Generated items are gated by a documented item-writing standard - passage-dependency validity, distractor trap types, lexical coverage - then by automated answer-key and multi-trait quality checks that fail closed. In soft launch with real learners.
talata
AI essay scoring and feedback - a fine-tuned DeBERTa-v3 dimensional scorer giving four trait bands plus an overall estimate at held-out QWK 0.76, MAE 0.63, with tail-preserving isotonic calibration and two grammar-correction models on Modal. Free and non-commercial; a commercially clean holistic checkpoint (frozen QWK 0.85) is kept working as the switch back.
cefr-workshop
A seven-part workshop teaching engineers to fine-tune DeBERTa-v3 for CEFR level prediction on serverless GPUs - problem framing through to deployment. MAE 0.375, QWK 0.776, 99.6% adjacent accuracy.
github-org-metrics
Python CLI for DORA metrics, PR cycle times, code churn and contribution patterns across an organisation. I wrote it to measure my own team at Cambridge, and it is what their delivery figures were reported from.
Full portfolio on the home page - everything above is live, documented and tested.
last updated: 2026-07