Remote AI Engineer for Healthcare RAG & LLM Apps

Eliassen Group; Olympia (WA); Remote; USD 125,000 - 165,000; Full time

Job summary

Eliassen Group is seeking an AI Engineer to build, test, and deploy AI-powered solutions addressing healthcare challenges within the PACE program. The role focuses on enterprise AI models to power applications like participant context engines, RAG pipelines, and agentic workflows.

Responsibilities include translating emerging AI capabilities into production-ready solutions, collaborating with stakeholders, and supporting team culture.

Qualifications

• Minimum three years of hands-on AI/ML engineering experience.
• Demonstrated experience deploying RAG pipelines with vector stores.
• Experience building on commercial LLM APIs with prompt engineering and context management.
• Advanced Python for AI applications, APIs, and data pipelines.

Responsibilities

• Design, build, and deploy AI-powered applications using enterprise LLMs for PACE business requirements.
• Architect and implement RAG systems with proprietary data ensuring accuracy, relevance, and compliance.
• Own the development lifecycle from ideation to production deployment.
• Research and build agentic AI workflows using LangGraph, LangChain, or Copilot Studio.
• Architect and deploy AI services in private Azure cloud environments with secure configurations.
• Evaluate models across performance, cost, latency, and safety considerations.
• Implement AIOps and MLOps with monitoring, versioning, CI/CD.

Skills

Python

Azure

Docker

LangChain

LangGraph

MLOps

CI/CD

LLM APIs

RAG pipelines

Vector databases

Prompt engineering

Security/compliance

Agile/Scrum

Communication skills

Education

Bachelor's Degree in Computer Science or related field

Master’s Degree preferred

Tools

Azure DevOps

Docker

LangChain

LangGraph

Copilot Studio

Job description

Eliassen Group is seeking an AI Engineer to build, test, and deploy AI-powered solutions addressing healthcare challenges within the PACE program. The role focuses on enterprise AI models to power applications like participant context engines, RAG pipelines, and agentic workflows.

Responsibilities include translating emerging AI capabilities into production-ready solutions, collaborating with stakeholders, and supporting team culture.