Senior AI/ML Engineer, Applications & Automation

We are seeking a Senior AI/ML Engineer to design, build, deploy, and evolve AI models, agents, and workflow automation for clinical terminology and content operations. This hands-on role combines AI/ML development with production ownership, taking models and agents from experimentation to reliable production use. The ideal candidate has experience with large language models, agent frameworks, retrieval-augmented generation, and the infrastructure and controls required to operate AI systems reliably.

Develop machine learning models, agents, and automation workflows for terminology management, content creation, mapping, and validation — evolving them from experimentation into scalable production systems. Build agentic workflows that use LLMs, tools, APIs, knowledge sources, retrieval capabilities, and structured business rules to complete complex tasks. Build and maintain retrieval-augmented generation solutions, vector and semantic search capabilities, and prompt and context-management strategies. Partner with our data science team to understand, integrate, and productionize their existing agents, and bring your own model and agent development to the team's roadmap. Own the deployment, monitoring, troubleshooting, and continuous improvement of AI workflows in production, including root-cause analysis and durable remediation of failures or unexpected outputs. Design evaluation, testing, and observability practices for AI systems, and implement controls for auditability, explainability, and human-in-the-loop review in clinically sensitive workflows. Develop cloud-based solutions using AWS services such as Amazon Bedrock, SageMaker, and Lambda, applying CI/CD, containerization, automated testing, and secure development practices. Work closely with clinical, mapping, product, data science, and engineering partners to translate workflows into practical solutions — and help define where AI automation is appropriate, where deterministic logic is required, and where human review must remain.

5+ years across AI/ML engineering, data science, machine learning engineering, or related disciplines, with a foundation in applied machine learning. Hands-on experience building agents and agentic workflows, including orchestration and tool or function calling. Hands-on experience building RAG solutions, including embeddings, vector databases, semantic search, and context engineering. Hands-on MLOps experience taking models and agents into production — deployment, versioning, monitoring, and CI/CD across multiple environments. Strong Python proficiency and experience developing maintainable services, APIs, pipelines, or workflow automation, plus working knowledge of SQL and relational databases such as PostgreSQL. Experience with cloud-based AI infrastructure, preferably AWS and Amazon Bedrock. Strong troubleshooting and root-cause analysis skills, and the ability to partner with domain experts and convert ambiguous workflow needs into scalable technical solutions. Clear written and verbal communication in cross-functional environments.

LangChain or LangGraph, LlamaIndex, OpenSearch, vector databases, or evaluation frameworks. Multi-agent or tool-using workflows, including state management, memory, routing, and failure recovery. Testing and evaluation approaches for non-deterministic AI systems. Healthcare technology, clinical terminology, clinical data normalization, mapping workflows, or regulated data environments. Familiarity with healthcare data standards such as knowledge graphs, FHIR, SNOMED CT, LOINC, RxNorm, ICD-10, or CPT. AI solutions incorporating human review, auditability, explainability, and quality governance.

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