Hearst
Principal AI Engineer
Dallas, TX, United States +1 · Principal · Full-time · Posted Oct 30, 2026
- Location
- Dallas, TX, United States
Dallas - Experience
- 10+ years in software engineering and 3+ years building and operating production AI systems; recent hands-on coding required.
- Education
- Bachelor’s or advanced degree in Computer Science, Engineering or a related field, or equivalent practical experience.
- Visa sponsorship
- This position does not provide immigration sponsorship. Applicants must have the right to work in the U.S. without immigration sponsorship.
About the job
Build and operate a shared AI agent platform that automates software engineering workflows and supports AI capabilities in healthcare products.
What you'll do
- Develop the agent platform, shared framework, tools, APIs and MCP integrations for engineering teams.
- Establish context, evaluation, safety, observability and operational standards for AI systems, including protection of regulated data.
- Mentor engineers, integrate AI into products and shape the AI technical roadmap.
What they ask for
- 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; 3+ years building and operating production AI systems in which agents call tools or change code.
- Proficiency in Python and modern AI tooling; hands-on MCP server or equivalent tool-interface experience, including authentication and per-tenant access control.
- Experience deploying and operating cloud AI workloads; proven RAG experience; AI evaluation and observability experience; working knowledge of regulated data such as PHI.
Nice to have
- Experience integrating AI into C# and .NET applications.
- Familiarity with vector databases, LLMOps and evaluation tooling, A2A, or large legacy codebases.
Tools
.NET, Azure, Azure DevOps, Kubernetes, Azure AI Foundry, MCP, Python, LangChain
AI focus
Build agentic AI software engineering workflows and product integrations; develop context and retrieval, evaluation, safety, observability and cost controls for production AI systems.
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