CV
The whole thing, in order.
AI and data-science master's student in Paris, and a freelance consultant who installs AI properly inside small businesses. I work across the stack, from retrieval and evaluation research through to the software and operating systems that put it to use. Second author on a paper accepted for oral presentation at IEEE Conference on Games 2026.
Experience
2026 – Present
Pre-Sales AI Security Consultant
Cybersecurity consultancy · Remote
Designed a Shadow AI exposure assessment methodology, and built the research and outreach pipeline that feeds it. Consulting on AI governance and on GDPR, EU AI Act and NIS2 compliance for organisations in regulated industries.
Shadow AI Exposure Assessment2025 – Present
Freelance AI Consultant
Independent · Paris, Remote
I install Claude inside small businesses: one folder on the client's own machine that their AI reads and writes, built around how the business actually runs. Five completed engagements, ranging from a company two weeks old to one carrying years of files and no system that had ever stuck.
2025 – Present
AI Consultant
Marketing agency · Remote
Built a retrieval-augmented content system and the application that runs it, then measured whether the retrieval actually worked. Written up as a master's internship report, including a result I had to retract.
Does Retrieval Make Generated Copy Sound Like the Client?Sept 2024 – June 2025
Teaching Assistant
Charles University, Prague · Prague, Czechia
Ran lab sessions and marked coursework for undergraduate computer science. Explaining the same idea to twenty people who each misunderstood it differently is the closest thing to consulting practice I have had.
Education
2025 – 2027
MSc AI & Data Science
Université Paris-Saclay · Paris, France
Machine learning, deep learning, information theory, information retrieval and data engineering, taught in English. M1 completed June 2026 with an overall mark of 15.097/20; M2 runs to June 2027.
2021 – 2025
BSc Computer Science with specialisation in Artificial Intelligence (First-Class Honours)
Charles University, Prague · Prague, Czechia
Where I learned to code, and where I got into machine learning in 2022, a few months before ChatGPT came out. Algorithms, software engineering and mathematical logic, with the artificial intelligence specialisation on top. I'd done a year of business science before this and preferred this by a distance. I also spent an academic year running the weekly lab sessions for an undergraduate course here, about twenty students a time, and it was the first time I'd had to explain any of this to people who didn't already get it. The thesis, on procedural terrain generation, became the IEEE paper below. I won two public-speaking competitions while I was there.
Constraint-Aware Terrain Generation2020 – 2021
Business Science
University of Cape Town · Cape Town, South Africa
A year of business science (statistics, economics and finance) before moving to Prague for computer science. It's why the commercial side of this work isn't something I had to learn later.
Publications & reports
2026
Constraint-Aware Terrain Completion from Sparse Fixed Regions for Game Heightmaps
Petr Šimůnek, Oliver Wakeford
IEEE Conference on Games 2026 (CoG), Madrid
Accepted for oral presentation, full paper
Second author. I built the terrain tool and proposed the constraint-propagation idea in my BSc thesis; my supervisor extended it into the paper, ran the experiments and led the writing. Reviewers noted the evaluation is limited to a single authored benchmark.
2025
Procedural Terrain Generation with Fixed Points
Oliver Wakeford
BSc thesis, Charles University, Faculty of Mathematics and Physics
Defended 5 September 2025
2026
Does retrieval make generated copy sound like the client?
Oliver Wakeford
Master's internship report, ACL format
Submitted July 2026. Not peer-reviewed, and not published
An internship deliverable assessed by the university, not a publication. The corpus belongs to a client and can't be shared.
Skills
Graded rather than listed. Core means I would take a hard problem in it; familiar means I have shipped something and would need a day to warm up.
AI & machine learning
- Retrieval & RAGCore
- Model evaluationCore
- LLM application designCore
- Deep learning (PyTorch)Working
- Fine-tuning (LoRA / PEFT)Working
- Generative modelsWorking
- AI governance (GDPR, EU AI Act, NIS2)Working
Development
- PythonCore
- TypeScriptCore
- Next.js / ReactCore
- SQLite / PostgreSQLWorking
- Flask / FastAPIWorking
- Unity / C#Familiar
Tools & platforms
- Claude API & Claude CodeCore
- GitWorking
- VercelWorking
- SupabaseWorking
- Tailwind CSSWorking
- JupyterFamiliar
Consulting & business
- Technical writingCore
- Client-facing deliveryCore
- AI strategyWorking
- Pre-salesWorking
- Public speakingWorking