Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to own LLM-powered systems end-to-end - from a first proof of concept built in days, to a production system a client trusts with their core workflows. You will work inside AI Labs, where most engagements start as an ambiguous business problem (complex legal documents, scanned and digital; extraction, classification, assistants, agents) and end as a working system with measurable quality.

• →5+ years of professional software engineering experience, with production ownership — not notebooks handed off to someone else.
• →2+ years building LLM-powered systems in production or in delivered client engagements: prompt engineering, structured extraction, RAG, workflows, or agents.
• →Experience designing and running evals: building eval datasets, defining graded criteria, establishing baselines and regression thresholds, and using them as a release gate. Candidates who have only eyeballed outputs will not pass this bar.
• →Advanced Python: clean, tested, reviewable code; async, typing, packaging, dependency management.
• →Hands-on MCP experience and judgment on when to expose capability as a tool/server rather than hard-wire it.
• →Hands-on RAG experience: chunking, embeddings, vector stores, reranking and the ability to explain how retrieval quality was measured.
• →Experience with at least one LLM orchestration framework (LangChain/LangGraph, LlamaIndex, Pydantic AI, or equivalent) and the judgment to work without one.
• →Cloud deployment of AI/ML workloads, preferably AWS (Bedrock, SageMaker, Lambda, ECS, S3) or demonstrable equivalent on GCP/Azure.
• →CI/CD and engineering hygiene: Git, automated tests, reproducible builds, containerization; ability to ship without a platform team holding your hand.
• →Practical understanding of cost and latency engineering for LLM systems: token accounting, caching, model routing, streaming.
• →Awareness of LLM security and privacy risks: prompt injection, data leakage, PII handling in a client-data context.
• →AI-first working style: daily hands-on use of AI coding tools (Claude Code, Cursor, Copilot, or similar) with the judgment to know when to trust the output.
• →Senior judgment: operate autonomously, challenge an ill-posed problem, prioritize by risk and business impact, own outcomes.
• →Excellent written and spoken English; comfortable in a fully remote, distributed, client-facing team.

Preferrable:
- Experience with an eval or LLM-observability framework (LangSmith, Langfuse, Weave, Braintrust, Arize Phoenix, DeepEval, Ragas, or similar).
- Experience building LLM-as-judge graders and validating judge/human agreement.
- Fine-tuning / adaptation experience (LoRA, SFT, distillation) and a clear view of when it pays off versus prompting or RAG.
- Hands-on deep learning: PyTorch, with models trained on real projects.
- Classical ML and NLP depth: classification, NER, embeddings, recommendation, computer vision, time series.
- MLOps tooling: MLflow, Weights & Biases, DVC, model/prompt versioning and registries.
- Serving open-weight models (vLLM, Ollama, Bedrock/SageMaker endpoints) and reasoning about GPU cost.
- Voice / real-time interfaces (STT, TTS, streaming agents).
- Legal, financial, or other regulated-domain document experience.
- Consulting or client-facing delivery experience; live demos, discovery, pre-sales.
- Public evidence of craft: open-source contributions, papers, talks, or a track record of writing about applied AI.