AI Solutions Engineer

Who We Are: Managed Health Care Associates, Inc. (MHA) provides care communities access, solutions, and insights to help them run their businesses more effectively. Our members include post-acute providers across the care continuum, including long-term care, home infusion, specialty pharmacies, senior living, and other group living facilities. Our team of associates is passionate about our common mission of helping people age with grace and championing our core values of being Curious Learners, Selfless Advocates, and Relentless Finishers.

 

Who we’re looking for: MHA is transforming its Technology division from a traditional, siloed waterfall model into an integrated, AI-first, agentic software delivery organization built on the Microsoft Azure and M365 ecosystem. As the first dedicated AI engineering hire within the Solution Engineering department, the AI Solution Engineer reports to the Director of Solution Engineering and serves as the hands-on technical driver who turns the Agentic SDLC vision into working practice across MHA’s engineering squads.

This is a lead/solution engineer role—not a support function. The person in this seat drives MHA’s agentic transformation, personally builds foundational tooling and guardrails, and serves as the organization’s subject-matter expert on agentic AI engineering, setting standards that squads adopt while those squads retain day-to-day delivery ownership. The role is equal parts builder and evangelist: standing up secure, well-governed CI/CD pipelines; integrating agentic workflows into GitHub, Visual Studio Code, and GitHub Copilot as the engineers’ day-to-day “agentic harness”; and continuously identifying high-leverage ways to apply AI to accelerate MHA’s products and internal delivery.

Success in this role advances MHA’s core engineering principles—AI-First Development, Shift-Left Quality, You Build It You Own It, and Azure-Native by Default—and supports enterprise targets such as the year-end goal of 50% agentic code check-in. Because MHA operates in a healthcare-adjacent environment, this role also ensures agentic tools and workflows comply with MHA’s responsible AI use policy and applicable data privacy requirements, including safeguarding PHI and other regulated data.

What You’ll Be Doing: Agentic Engineering, Orchestration & Context Design

• Establish and continuously refine the “agentic harness,” the integrated toolchain of GitHub, VS Code, GitHub Copilot, and Claude, that full-stack Digital Solutions Engineers use for AI-assisted, agentic development.
• Design and document agent orchestration patterns, including multi-agent and subagent delegation, MCP (Model Context Protocol) tool integration, and context engineering practices, that let engineers reliably delegate implementation, testing, and review work to AI agents.
• Partner with squads to increase agentic code check-in rates in support of MHA’s year-end adoption target, tracking and reporting progress through the METL framework.
• Serve as the internal subject-matter expert on agentic coding practices, staying current on emerging AI development tools, MCP servers, and agent frameworks, and rapidly evaluating them for fit within MHA’s Azure-native environment.
• Design agentic workflows and tool integrations with interoperability in mind, ensuring agents, MCP servers, and enterprise systems (Jira, Azure DevOps, and platform APIs) orchestrate cleanly across squads rather than functioning as disconnected point solutions.

Evals, Quality & Responsible AI Use

• Build and own MHA’s evaluation framework for agentic and AI-generated output, including automated evals, regression test suites, and human-in-the-loop (HITL) checkpoints for higher-risk agentic changes, closing what is currently one of the org’s largest technical capability gaps.
• Define quality bars and pass/fail criteria for agentic contributions prior to merge, working alongside the SonarQube and Playwright gates already embedded in the pipeline.
• Ensure agentic workflows and AI tool usage comply with MHA’s responsible AI use policy and applicable data privacy requirements, including safeguarding PHI and other regulated data from unintended exposure to AI tools or models.
• Partner with IT Operations & Security on data-handling guardrails (masking, redaction, access scoping) for any agentic workflow that could touch sensitive member, customer, or PHI-adjacent data.

**AI Security & Govern…