Senior AI Engineer LLM and Agentic Workflows

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IN

3 days ago Requisition

ELLKAY started out providing connectivity solutions to laboratories and within a few years, grew to also provide data management solutions to ambulatory organizations. ELLKAY is now a trusted data management partner in five healthcare segments. ELLKAY’s solutions continue to serve laboratories and ambulatory practices and have expanded to empower hospitals and health systems, healthcare IT vendors, ambulatory practices, health plans, and other healthcare organizations with cutting-edge technologies and solutions that drive their growth and interoperability strategies.

Today, ELLKAY remains true to our core values, building strong partner relationships and offering unparalleled service and support while providing innovative, scalable solutions to the challenges our customers face in today’s data-rich world. ELLKAY's experience, customer-focused approach, and reputation for innovation, speed, and accuracy differentiate ELLKAY as a premier partner for your interoperability needs and data management strategy.
Job Description: Build production LLM applications and agent workflows for implementation assistance, connector capability discovery, migration operations, and archive access. The engineer will implement retrieval, tool integration, workflow state, citations, approval checkpoints, and automated evaluation. The role connects assistants to existing ELLKAY services and the Central DME Portal while preserving tenant, purpose, and data-access boundaries.

• 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.

• 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.
• Hands‑on RAG, hybrid/vector search, embeddings, reranking, structured outputs, context management, and tool calling.
• Experience implementing agent state, timeouts, bounded retries, approval checkpoints, fallback, and duplicate‑action prevention.
• 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…