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

• Build scalable memory and retrieval systems for high-context AI workflows
• Hybrid retrieval across knowledge graphs, vector, lexical, and structured approaches
• Evaluation workflows comparing retrieval and memory strategies on real corpora
• Agent memory interfaces — enabling AI workflows to query, inspect, and reuse context
• Quality metrics: recall, provenance, latency, cost
• Routing logic — deciding when to use lightweight retrieval, deeper reasoning, or agentic workflows
• Fine-tuning, distillation, and model adaptation pipelines
• Observability for AI workflows: retrieval failures, model regressions, cost budgets