Machine Learning Engineer - Agentic AI

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Full Time US

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:

We’re looking for a versatile Full Stack AI/ML Engineer who can architect, build, and deploy production-grade AI/ML and Agentic AI systems on AWS infrastructure. This role combines deep expertise in foundation models, agentic frameworks, healthcare data standards, and serverless cloud architecture to create scalable, secure, and compliant AI solutions for healthcare organizations.

AI/ML & Agentic Engineering

• Agentic Frameworks & Architectures: Architect, implement, and deploy end-to-end Agentic AI systems utilizing tool calling, stateful multi‑agent collaboration, dynamic task routing, reflection loops, and self‑correction workflows using frameworks like Lang Graph, Auto Gen, CrewAI, or Bedrock Agents.
• Prompt & Tool Engineering: Design and implement production foundation model applications using advanced prompt engineering, structured output design, function‑calling patterns, dynamic tool retrieval, and hallucination mitigation strategies.
• Memory & Vector Search: Build and optimize vector search and long‑term memory solutions using Open Search Serverless with k‑NN, including HNSW/IVF parameter tuning, hybrid retrieval (lexical + semantic), and multi‑stage ranking pipelines (BM25 + dense + re‑ranking) for RAG and agent context retrieval.
• Bedrock & Guardrails: Develop and deploy Amazon Bedrock and Bedrock Agent solutions leveraging Claude family models, Titan Embeddings v2, and Guardrails for PHI/PII detection while optimizing for provisioned vs. on‑demand throughput.
• Classical ML: Implement classical ML pipelines including clustering algorithms (HDBSCAN, k‑means), confidence score calibration (Platt scaling, isotonic regression), and model evaluation frameworks.
• Design and implement systems that work with FHIR R4 resources, HL7 v2 message structures, and USCDI data elements.
• Build ontology retrieval and agent tool interfaces against LOINC, SNOMED, and RxNorm terminologies to support clinical decision‑making and data normalization.
• Ensure HIPAA compliance and implement appropriate security controls for protected health information (PHI) across LLM inputs, tool payloads, and agent memory states.
• Build high‑performance APIs and agent execution backends using FastAPI and Pydantic v2 with async I/O patterns, robust retry/backoff mechanisms, state management, and structured error handling.
• Design and deploy AWS serverless architectures including Lambda (Python + container), API Gateway (REST + HTTP), Step Functions, Event Bridge, SQS, and SNS.
• Implement container‑based…