AI ML Engineer

100% Remote Contract Duration: 06 Months Contract

Full Stack- AI ML Engineer-Agents & Retrieval

What you'll do: Build the DKB Query API as UC Functions: dkb_search, dkb_lookup, dkb_content (Phase 1); dkb_impact UC Stored Procedure (Phase 2, if graph extension triggered) Implement semantic search over the knowledge graph using Mosaic AI Vector Search Expose DKB tools via endpoint, MCP, or A2A so technical IDEs (e.g., Cursor), agents, and functional applications can consume them Design and implement agent failure/fallback behavior (empty results, stale data, traversal timeouts) Set up agent evaluation using Mosaic AI Agent Evaluation and MLflow 3.0 Build agent tracing and observability (query latency, accuracy metrics, usage dashboards) Work with domain users to validate DKB-powered design scenarios (future of supply planning, migration assessments, architecture Q&A) Must-have skills: 3+ years building AI/ML applications, with at least 1 year on LLM-based systems (RAG, agents, tool calling) Experience with Databricks Mosaic AI (Vector Search, Agent Framework, or Foundation Model APIs) Python fluency -- building production-quality agent tools, not just notebooks Understanding of semantic search: embeddings, chunking strategies, retrieval evaluation (precision, recall, relevance) Experience with MCP (Model Context Protocol) or similar tool-calling patterns Comfortable evaluating AI system quality (golden datasets, A/B comparison, human-in-the-loop review) Nice-to-have: Experience with MLflow (especially MLflow 3.0 agent tracing) Experience with UC Functions as agent tools Familiarity with technical IDEs (e.g., Cursor) or functional applications that consume agent tools Prior work on enterprise knowledge retrieval or domain-specific RAG systems