AI Engineer I

Welbehealth Non Sponsored Board

10d•$90,281 - $119,171

About The Position

The AI Engineer I is responsible for supporting the design, development, testing, and implementation of AI-powered solutions that address operational and participant care challenges within the PACE (Program of All-Inclusive Care for the Elderly) program. Working under the guidance of senior engineers, the role assists with developing and maintaining enterprise AI models, retrieval-augmented generation (RAG) systems, and agentic workflows. The AI Engineer I collaborates effectively with colleagues and stakeholders to promote WelbeHealth values, team culture and mission. This role is different because the AI Engineer I at WelbeHealth: Helps build innovative AI-powered solutions that improve participant care and operational efficiency by developing enterprise applications using large language models (LLMs), retrieval-augmented generation (RAG), and emerging agentic AI technologies in a mission-driven healthcare environment. Works alongside experienced AI engineers and architects to rapidly prototype, test, and deploy modern AI solutions while gaining hands-on experience with cloud platforms, enterprise AI frameworks, and responsible AI practices that directly support the future of healthcare delivery.

Requirements

Bachelor’s Degree in Computer Science, Data Science, Software Engineering, or a related field required
Working proficiency in Python, including the ability to write and debug scripts, interact with APIs, and work with common data structures
Foundational understanding of artificial intelligence and machine learning concepts, including embeddings, vector search, prompt engineering, and retrieval techniques
Exposure to large language model (LLM) platforms and APIs such as OpenAI, Anthropic, or Google Gemini through academic projects, internships, personal projects, or professional experience

Nice To Haves

Relevant coursework or bootcamp training is a plus
Coursework or self-directed study in machine learning, natural language processing, or AI application development is a plus
Relevant certifications (e.g., Azure AI Fundamentals, AWS Cloud Practitioner, Google ML) are a plus
Exposure to cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP) preferred
Familiarity with retrieval-augmented generation (RAG) concepts and vector database such as Pinecone, Weaviate, Chroma, or similar tools preferred
Exposure to AI orchestration or agentic frameworks such as LangChain, LangGraph, or similar technologies preferred
Experience using version control tools such as Git and familiarity with basic CI/CD concepts and development workflows preferred
Basic understanding of healthcare data privacy and security requirements, including HIPAA and protected health information (PHI) handling practices preferred

Responsibilities

Assist in building and testing AI-powered applications using enterprise LLMs (OpenAI, Anthropic Claude, Google Gemini) under the guidance of senior engineers.
Help translate PACE program business requirements into early-stage AI prototypes and proofs of concept.
Support prompt engineering, structured output handling, and basic function/tool calling integrations.
Contribute to the development and testing of retrieval-augmented generation (RAG) systems that ground AI responses in WelbeHealth’s proprietary data.
Assist with vector database setup, embedding strategies, and chunking optimization under senior direction.
Help evaluate retrieval quality and document findings.
Participate in rapid POC development cycles, contributing code, documentation, and iterative improvements.
Assist in validation and QA testing of new AI use cases.
Actively seek feedback, ask questions, and apply learnings to improve output quality.
Support cloud-based deployments in Azure environments with guidance on Docker, private endpoints, and secure configurations.
Learn and follow best practices for AI/ML operations, including monitoring, versioning, and CI/CD pipelines.
Assist in maintaining documentation for AI services and infrastructure.
Stay curious about emerging AI tools, model releases, and techniques.
Share relevant findings with the team and help evaluate new approaches that could benefit participants and operations.
Participate in team demos, retrospectives, and knowledge-sharing sessions.