Full Stack- AI ML Engineer-Agents & Retrieval

About the job

100% Remote Contract Duration: 06 Months Contract 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

Requirements

• AI/ML Applications
• Python
• Semantic Search
• Databricks Mosaic AI

Preferred Technologies

• AI/ML Applications
• Python
• Semantic Search
• Databricks Mosaic AI