Senior AI/ML Engineer, Applications & Automation

 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.