Build RAG and tool-calling workflows for implementation assistance, connector capability discovery, error explanation, and guided migration operations.
Create context assembly from approved documentation, run status, mapping metadata, and evidence. Return citations and structured outputs.
Implement MCP clients/tool integrations and thin adapters over platform contracts. Handle async request IDs, retries, timeouts, and duplicate action prevention with the orchestration team.
Implement permission-aware retrieval, approval checkpoints, prompt-injection defenses, bounded tool use, and explicit failure or abstention behavior.
Capture agent traces and user feedback, and add regression tests for prompts, tool selection, retrieval quality, and task completion.
Improve retrieval and context assembly using measured failures. Evaluate rerankers or model adaptation when simpler prompting and retrieval changes do not meet quality needs.
Partner with Data Platform and product engineers, clinical/data SMEs, DME/QA, and security teams. Build reusable services for ELLKAY products and preserve source evidence, approved data-use rights, and tenant isolation. Model or agent proposals follow established human approval and platform release workflows.
Qualifications
5+ years in software/AI engineering, including production LLM applications and backend services. The total experience requirement does not imply five years in generative AI alone.
Strong Python, REST APIs, asynchronous programming, data handling, software testing, and production debugging.
Ability to evaluate retrieval relevance, groundedness, tool selection, task completion, latency, and unit cost using representative tests.
Experience deploying services to a cloud environment and implementing authorization-aware retrieval and PHI-safe telemetry. A relevant degree or equivalent practical experience is acceptable.