AI Engineer

Summary

WelbeHealth is a value-based healthcare organization providing comprehensive care and services to vulnerable seniors through the PACE program. The AI Engineer will build, test, and deploy production-ready AI applications for healthcare challenges, including RAG systems, participant context engines, and agentic workflows. The role also involves secure cloud deployment, LLM evaluation, and implementation of AIOps and MLOps practices.

Responsibilities

• Design, build, and deploy AI-powered applications using enterprise LLMs (OpenAI, Anthropic Claude, Google Gemini), as well as translate PACE business requirements into production-ready AI solutions
• Architect and implement RAG systems that ground AI responses in WelbeHealth's proprietary data, ensuring accuracy, relevance, and compliance with healthcare data standards
• Own the full development lifecycle for new AI use cases from ideation and rapid POC development through validation, iteration, and production deployment
• Research and build agentic AI workflows (using frameworks such as LangGraph, LangChain, or Copilot Studio) that evolve our systems toward autonomous, goal-oriented agents capable of handling complex multi-step healthcare processes
• Architect and deploy AI services within private cloud environments (primarily Azure; AWS as needed), utilizing Docker containers, private endpoints, managed identities, and secure VNET configurations
• Evaluate and integrate across the frontier model landscape, selecting the right model for each use case based on performance, cost, latency, and compliance requirements
• Implement AIOps and MLOps best practices monitoring, versioning, automated testing, and CI/CD pipelines to ensure all AI applications are reliable, scalable, and maintainable
• Continuously evaluate emerging AI tools, techniques, and model releases, as well as proactively recommend new approaches that can improve participant outcomes, operational efficiency, or developer productivity

Skills

• Bachelor's degree in computer science, AI, computer engineering, or other relevant field
• Minimum of three (3) years of hands-on experience in AI/ML engineering, applied AI development, or software engineering with a strong AI focus
• Demonstrated experience designing and deploying RAG pipelines, including vector databases, embedding strategies, chunking optimization, and retrieval evaluation
• Proven ability to build applications on top of commercial LLM APIs (OpenAI, Anthropic, Google) including prompt engineering, structured output handling, function/tool calling, and context window management
• Advanced proficiency in Python for AI application development, API integration, and data pipeline construction
• Hands-on experience with Azure AI services (AI Foundry, Managed Identities, Key Vault, private networking) and Docker-based deployments in secure cloud environments
• Proficiency with Azure DevOps (or equivalent) for building CI/CD pipelines that automate testing and deployment of AI applications
• Solid understanding of agentic architectures and frameworks such as LangChain, LangGraph, Semantic Kernel, or Copilot Studio
• Experience working in Agile/Scrum environments with iterative development cycles and rapid POC delivery
• Master's degree
• Familiarity with HIPAA, PHI handling, and the compliance requirements unique to healthcare technology
• Understanding of the PACE model of care and how technology can improve participant outcomes and operational workflows
• Experience benchmarking and comparing LLM providers across dimensions such as accuracy, cost, latency, and safety
• Secondary cloud experience with AWS AI/ML services

Qualifications

Must Haves

• Bachelor's degree in computer science, AI, computer engineering, or other relevant field
• Minimum of three (3) years of hands-on experience in AI/ML engineering, applied AI development, or software engineering with a strong AI focus
• Demonstrated experience designing and deploying RAG pipelines, including vector databases, embedding strategies, chunking optimization, and retrieval evaluation
• Proven ability to build applications on top of commercial LLM APIs (OpenAI, Anthropic, Google) including prompt engineering, structured output handling, function/tool calling, and context window management
• Advanced proficiency in Python for AI application development, API integration, and data pipeline construction
• Hands-on experience with Azure AI services (AI Foundry, Managed Identities, Key Vault, private networking) and Docker-based deployments in secure cloud environments
• Proficiency with Azure DevOps (or equivalent) for building CI/CD pipelines that automate testing and deployment of AI applications
• Solid understanding of agentic architectures and frameworks such as LangChain, LangGraph, Semantic Kernel, or Copilot Studio
• Experience working in Agile/Scrum environments with iterative development cycles and rapid POC delivery

Nice to Haves

• master's degree
• Familiarity with HIPAA, PHI handling, and the compliance requirements unique to healthcare technology
• Understanding of the PACE model of care and how technology can improve participant outcomes and operational workflows
• Experience benchmarking and comparing LLM providers across dimensions such as accuracy, cost, latency, and safety
• Secondary cloud experience with AWS AI/ML services

Benefits

• Medical insurance coverage (Medical, Dental, Vision)
• Work/life balance - 17 days of personal time off (PTO), 12 holidays observed annually, and 6 sick days
• 401K savings + match
• Bonus as part of the comprehensive compensation package
• A competitive total rewards package that includes a 401(k) match, comprehensive healthcare coverage, and a broad range of additional benefits