LLM application engineering
RAG, vector databases, agent tool-use, MCP servers, prompt and context engineering, lightweight evals. End-to-end systems, not toy demos.
RAGMCPLangChainPineconeEvals
Available for freelance - EU / remote
Senior Python engineer building LLM applications and agentic tooling.
15 years of production Python in studios where code had to ship under deadline pressure; now building the LLM application and agent infrastructure layer on top of it.
Previously at
The LEGO GroupScanline VFXDNEGNetflixGimbal Goats
What I do
Four ways I show up on engineering teams.
Agentic developer workflows
Harnesses, custom CLIs, and skills around AI coding assistants so engineering teams can actually use them.
Claude CodeCodexCLITeam enablement
Production Python and practical polyglot work
FastAPI backends, data pipelines, microservices. Python is the through-line; Go for single-binary CLIs, TypeScript where it earns its place, C++ at the engine layer.
PythonGoTypeScriptFastAPIAWSDockerCI/CD
Technical advisory
Sounding-board for founders or early CTOs working on agentic workflows or LLM tooling. Low-volume, direct, practical.
HourlyRetainer
Selected work
Featured project, then the rest of the shelf.
Public case studyOpen source
Maya MCP server
Open Model Context Protocol server that lets AI agents act inside Autodesk Maya: read scene state, run tools, verify their own work.
Protocol-first, model-agnostic. Documented in a 3-part public case study including a real production-crash debugging walkthrough.
PythonMCPMayaOpen source
maya-mcp - live tool-call stream
Closed source
Kaiwa
End-to-end RAG chatbot on Django and Celery.
Apache 2.0
ActionRail
Viewport UI framework for Autodesk Maya.
Open source
MayaCythonCli
CLI that compiles Python codebases into Cython extension packages.
Open source
goBankCli
Go CLI that connects to your local bank to read balances and transactions from the terminal.
Production 2024-2025Closed source
LEGO SKU-to-Maya pipeline
Python data pipeline on AWS that ingests 3D product models, processes them through Pixar USD, and stores queryable asset data in MongoDB.
Agentic dev-workflow rollout
Internal harnesses, custom CLIs, and bundled agent skills around Claude Code and OpenAI Codex.
Writing
Public notes and case studies.
What is Maya MCP?
Why Autodesk Maya needed its own Model Context Protocol server, and how the protocol-first design keeps it model-agnostic.
ReadInside Maya MCP: architecture and examples
How agents call tools, verify their own work, and what the tool surface looks like in practice. With live transcripts.
ReadDebugging a Maya crash with Maya MCP
A real production-crash walkthrough where the agent identifies the offending node faster than a manual bisect.
ReadA Hugo and Cloudflare Pages deployment stack
How this writing surface is deployed. Cheap, fast, repeatable.
Read About
Fifteen years of shipping.
I'm a senior Python engineer based in Belgium. My work centers on building LLM applications and the agent tool-use systems around them, owning production Python stacks, and rolling out agentic developer workflows for engineering teams.
Most recently I built an open-source MCP server that lets AI agents operate inside Autodesk Maya, with a 3-part public case study documenting it. I also built Kaiwa, a production RAG chatbot on LangChain and Pinecone with hybrid retrieval and Cohere reranking.
Before that, I spent 15 years shipping Python software in studios where code had to run reliably under deadline pressure: The LEGO Group, Scanline VFX, DNEG, and earlier studios in Belgium and the Netherlands.
Python is the through-line, but the toolbox is wider when it helps: Go for single-binary CLIs, TypeScript and SQL where they earn their place, C++ down at the engine layer.
Available for freelance engagements via my own company.
Outside the editor: self-hosted Home Assistant, ESP32 tinkering, a 3D printer in the workshop.
2025-now Co-founder, CTO and Software DeveloperGimbal Goats (transitioning to advisor)
2020-now Freelance Software DeveloperOwn company
2024-2025 Software EngineerThe LEGO Group - Tools and Systems
2021-2024 Software DeveloperScanline VFX - Rigging Dept.
2018-2021 Software Engineer (Python tooling)DNEG - London / Vancouver
2011-2018 Python tools and 3D developmentGrid-VFX, Cyborn, Walking the Dog, Cruden B.V.
FAQ
How engagements usually work.
Short answers for the questions that tend to come before a first call.
EU / remote Freelance Production code
What problems are the best fit?
The best fit is production engineering around LLM applications, retrieval systems, MCP servers, agent tool-use, developer workflows, and Python-heavy backends. I am most useful when the work has to move from experiment to shipped system inside an existing product or team.
Are you limited to Python?
No. Python is the through-line, but I regularly work across TypeScript, Go, SQL, shell, Docker, AWS, CI/CD, vector databases, and LLM APIs. I can also operate near C++ or engine/tooling boundaries when the project needs it.
Can you productionize an LLM prototype?
Yes. A common engagement is taking a promising prototype and turning it into something testable, observable, maintainable, and connected to real data or workflows. That can include retrieval quality, tool boundaries, evals, deployment, cost control, and failure handling.
Can you work inside our team?
Yes. I am used to joining existing codebases, review habits, CI, stakeholder constraints, and production deadlines. I can own a narrow subsystem, pair with internal engineers, or act as the senior contributor who gets a first slice over the line.
Do you advise or also build?
Both, but the default is hands-on engineering. Advisory works best when you need technical direction, architecture review, or a second opinion; implementation works best when there is a concrete workflow, repo, integration, or launch target.
What should we bring to a first call?
Bring the problem, the current state, the constraints, and what a useful first result would look like. A repo shape, architecture sketch, failing workflow, sample data, or rough product goal is enough to decide whether the next step should be an audit, prototype, or implementation slice.
Got an agentic problem to ship?
Freelance availability via my own company. Rates on request. Best by email; I read everything that lands.
bram.van.rompuy@gmail.com