About this role

Senior ML/AI Engineer iTeamly Daily tasks: - 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 Musts: AI, Python, Neo4j 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

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