Principal AI Engineer at Hearst · LumiJobs

Responsibilities

Homecare Homebase (HCHB) is the software platform behind home health and hospice care across the United States, supporting scheduling, clinical documentation, coding, compliance and revenue cycle for agencies serving millions of patient visits each year.
We are seeking a Principal AI Platform Engineer to build our AI software factory. This is a hands-on individual contributor role. You will write code every day, ship the first working version of our agent platform yourself, and then teach engineering teams to build on it. You will have no direct reports at the start. The platform will run agents against our production codebase, with people writing specifications and validating results and agents doing most of the implementation. You will also serve as the senior technical authority on AI built into our products.
Our primary stack is .NET and Azure, with Azure DevOps, Kubernetes, Azure AI Foundry and MCP-based tooling, alongside other languages and frameworks. The agent platform must work across all of them. This is a greenfield effort, so you will set the standards rather than inherit them.
Is This Role for You?
This role is for someone who builds. You’ll set standards, shape how teams use AI and influence the roadmap, but you’ll do it through working code and reference implementations, not decks, review boards or org charts. If your recent work has mostly been leading teams, advising executives or designing solutions that others implement, this probably isn’t the right match. Expect to spend most of your time coding, especially in your first few months.
AI Platform Architecture and Development
Design, write and operate the agent platform that runs autonomously against our production codebase, taking work from specification to pull request with human review.
Implement agentic patterns, orchestration and tool integration for real-world engineering workflows.
Make the platform effective across our full technology stack, not only one language or framework.
Define the agent templates and shared framework that every team uses.
Own the AI lifecycle from concept through deployment, operation and iteration on Azure and Azure AI Foundry.
Shape how work is handed to agents and review the results, from concept to specification through pull request.

Qualification

10+ years in software engineeringSolid grasp of LLMsProven RAG experienceExperience integrating AI into C# andInterest in healthcareAdditional Information

Required

Bachelor’s or advanced degree in Computer Science, Engineering or a related field, or equivalent practical experience.
10+ years in software engineering, with recent hands-on coding. You write code today, not only review it.
3+ years building and operating production AI systems in which agents call tools or change code. RAG or machine learning products alone do not meet this bar.
Proficiency in Python and modern AI tooling (for example LangChain, LangGraph, MCP, OpenAI and Anthropic SDKs).
Hands-on experience building MCP servers or equivalent tool interfaces, including authentication and per-tenant access control.
Ability to make agents effective across multiple languages and technology stacks, not only one.
Hands-on experience deploying and operating AI workloads in cloud environments (Azure preferred).
Solid grasp of LLMs, generative AI, agentic patterns and context engineering.
Proven RAG experience, and judgment on when retrieval beats curated context.
Experience with AI evaluation and observability, including quality measurement, cost and token management.
Working knowledge of handling regulated data such as PHI in AI systems.
Demonstrated record of mentoring engineers and raising technical capability across teams.

Preferred

Experience integrating AI into C# and .NET applications.
Experience using AI coding tools such as Cursor or Claude Code on large codebases.
Familiarity with vector databases and retrieval tooling (for example pgvector, Pinecone, Azure AI Search).
Exposure to LLMOps and evaluation tooling (for example Azure AI Foundry, MLflow, LangSmith, CI/CD).
Familiarity with agent-to-agent communication standards such as A2A, and judgment on when a multi-agent design is warranted over a single agent with tools.
Experience with large legacy codebases and incremental modernization.
Interest in healthcare, EHR or revenue cycle applications.
Additional Information

Principal AI Engineer

Responsibilities

Qualification

Required

Preferred

Company

Hearst

More roles at Hearst