Principal AI Platform Engineer — Hands-On Azure Builder

Hearst Corporation

Dallas (TX)

Presencial

USD 180 000 - 280 000

Tempo integral

Há 2 dias

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Resumo da oferta

Homecare Homebase seeks a Principal AI Platform Engineer to build our AI software factory. This hands-on IC role writes code daily, ships the agent platform, and mentors teams. You will be the senior technical authority on AI in our products.

Our stack is .NET and Azure with Kubernetes, MCP tooling and Azure AI Foundry. You’ll set standards across languages, define templates, manage the AI lifecycle on Azure, and ensure scalable, observable, cost-conscious AI across the platform.

Qualificações

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 with agents calling tools or changing code.
Proficiency in Python and modern AI tooling (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.
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 retrieval vs 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 mentoring experience across teams.
Evidence of your own work: a repository, code sample or demo of agent or MCP work.

Responsabilidades

Design, write and operate the agent platform that runs autonomously against our production codebase, 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.
Partner with application teams to integrate AI capabilities into existing products and workflows.
Build the APIs, SDKs, MCP servers and extension points that engineering teams build on.
Establish how MCP servers, tools and agent skills are packaged, versioned, discovered and distributed to teams, such as through an internal registry or catalog with access controls.
Design tool access with per-tenant data scoping, so that an agent acting for one agency can reach only that agency’s data.
Design the context, skills and retrieval patterns that make AI agents effective in large codebases, including an application-level knowledge map that tells an agent which module a ticket belongs to and what it affects.
Decide when retrieval is the right tool and when curated context is better.
Establish versioning, review and governance for shared AI assets.
Build evaluation frameworks that measure AI output quality and detect regressions.
Define guardrails, failure handling and human-in-the-loop controls for regulated environments, including PHI data handling.
Monitor and optimize AI systems for reliability, accuracy and cost efficiency with token budgets and rate limits.
Establish observability best practices across supervised and autonomous AI usage.
Mentor engineers across the organization and raise AI engineering capability at scale with working examples.
Define engineering standards and shape the AI technical roadmap with the CTO.
Evaluate emerging capabilities in LLMs, agentic AI, RAG and MCP, and bring to production where proven.
Advise on build, buy and partner decisions across the AI toolchain.
Maintain a working view of commercial and open-weight models and match to tasks by cost and latency.