Senior Machine Learning Engineer

We're looking for a Senior AI Engineer to build the AI components of a governed data foundation used by AI agents, applications and business teams.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. Hence, this is a hybrid of engineer and scientist. You write production-grade Python and ship it through CI/CD, and you treat model quality as an engineering discipline: you do not say "it works" — you show the eval. Curated datasets, graded criteria, baselines, regression thresholds wired into CI. You know that an LLM feature without evals is an untested feature, and you build the measurement before you build the demo.

You are vendor-agnostic and cost-aware: you choose models based on business constraints, and design solutions between the whole catalog of in-context learning options and new available tools. You use AI tools daily to accelerate your own work, with the judgment to know when to trust the output and when not to. Because we are a consultancy company, you are also client-facing: you demo live, explain trade-offs to non-technical stakeholders, size effort honestly in pre-sales, and turn what you learn into assets the rest of the company can reuse. You might be needed to record videos, design prototypes, and help all through the creative process of a solution.

You'll work in a small team (solution architect, data engineer, full-stack engineer, data quality lead, product and project managers) alongside the client's engineers, who will run what you build.

Required Qualifications

- 5+ years of professional software engineering experience, with production ownership — not notebooks handed off to someone else.
- 2+ years building LLM systems used by real users or delivered to clients.
- Advanced Python: clean, tested, reviewable code; async, typing, packaging, dependency management.
- Structured extraction from unstructured documents.
- Hands-on RAG experience: chunking, embeddings, vector stores, reranking and the ability to explain how retrieval quality was measured.
- Hands-on Agents with tool use and MCP experience.
- Awareness of LLM security and privacy risks: prompt injection, data leakage, PII handling in a client-data context.
- 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.
- CI/CD and engineering hygiene: Git, automated tests, reproducible builds, containerization; ability to ship without a platform team holding your hand.
- 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.
- Practical understanding of cost and latency engineering for LLM systems: token accounting, caching, model routing, streaming.
- 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 AI in Databricks environments.
Permission-aware or multi-tenant retrieval for customer-facing AI.
Experience with an eval or LLM-observability framework (LangSmith, Langfuse, Weave, Braintrust, Arize Phoenix, DeepEval, Ragas, or similar).
Knowledge pipelines that stay in sync with changing sources, with human review.
Databricks and MLflow (tracing, evaluation).
Contracts, finance or regulatory documents.
Experience building LLM-as-judge graders and validating judge/human agreement.
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.
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.