Job Description

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.

WHAT YOU’LL DO

WHAT YOU’LL NEED

• 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.

PREFERRED QUALIFICATIONS

• 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.