Freelance ML engineer and AI strategy consultant based in Europe, working remotely with teams that need honest answers before they scale models or buy more GPU.
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AI strategy & readiness
For: leadership and product teams exploring ML or Gen AI without a clear internal owner.
Typical work:
- “Do we need AI for this?” — problem framing, feasibility, build vs buy, and risk (data, compliance, ops).
- Readiness assessment: data, team skills, existing tooling, and what a sensible first milestone looks like.
- Integration roadmap: phased plan from pilot to production, with explicit non-AI alternatives when they are the better fit.
Deliverables: written assessment, prioritized options, and a concrete next-step proposal (often 1–2 weeks).
MLOps audit & production ML review
For: teams with models in staging or production that lack reproducibility, monitoring, or release discipline.
Typical work:
- Pipeline review: training, evaluation, deployment, rollback, and experiment tracking (W&B, MLflow, Sacred, etc.).
- CI/CD and containerization gaps; observability and alerting for model/version drift.
- Recommendations ranked by impact vs effort, with optional hands-on implementation support.
Deliverables: audit report, remediation backlog, and optional follow-on implementation (2–4 weeks common).
Edge perception & real-time computer vision
For: robotics, logistics, and embedded teams shipping perception on constrained hardware.
Typical work:
- Detection, depth, and fusion pipelines on Jetson-class devices (TensorRT, latency budgets, sim-to-real).
- Synthetic data strategy, mAP/latency tradeoffs, and deployment hardening.
- Tech lead or IC delivery on perception stacks already in flight.
Deliverables: working pipelines, benchmarks, and documentation for handoff to your engineering team.
Private Gen AI & on-prem RAG
For: organizations that need document Q&A or agents without public-cloud dependency.
Typical work:
- Architecture for OCR → chunking → embeddings → retrieval (vLLM, Ollama, on-prem deployment).
- Evaluation of VLMs/RAG quality, privacy boundaries, and operational runbooks.
- Strategy for Drizz-style product research or docai.tools-style private knowledge bases.
Deliverables: reference architecture, PoC, or production path depending on scope.
How engagements usually run
| Phase | What happens |
|---|---|
| Intro call | 30 minutes — scope, constraints, whether there is a fit |
| Scoping | Fixed proposal: deliverables, timeline, rate (day-rate or fixed-scope) |
| Delivery | Remote, async-friendly; overlap with your timezone where needed |
| Handoff | Docs, code, or runbooks your team can own |
I work as an independent contractor (not agency resale). NDA and EU/GDPR-aware setups are routine.
Background
6+ years across ADAS perception, aerial segmentation, edge robotics (Verses AI, Edge Case Research, Fraunhofer AISEC, TUM). See full CV and PDF resume for roles, metrics, and publications.
To start: vyasdevms@gmail.com with a short note on your team, problem, and timeline.