Full Stack AI/ML Engineer - Agents and Retrieval

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

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• 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

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• 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