Senior ML/AI Engineer

5+ years in ML engineering / applied AI / search & retrieval / backend AI systems
Strong Python — production services, not just notebooks
Hands-on with LLMs: retrieval, embeddings, structured outputs, tool/agent orchestration
Experience building evaluation pipelines (golden cases, automated checks, human review loops)
Understanding of retrieval failure modes: stale context, hallucination amplification, ranking drift
Knowledge graphs / graph-based memory (Neo4j or similar)
APIs, async jobs, observability, schema validation, production debugging
Strong bias toward measurable quality improvements over demo-only progress
AI-native dev tools: Claude Code, Codex, or similar

Nice to Have

Fine-tuning, distillation, or local model deployment
Experience with sovereignty-driven model strategy
Agentic systems or long-term memory for AI products
Pragmatic reasoning across vector / lexical / graph / hybrid retrieval approaches

See also