AI Solutions Engineer

AI Solution Engineer

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.

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.

AI Solution Engineer Responsibilities:

• 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.

AI Solution Engineer Evaluations, 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 Solution Engineer AI Security & Governance:

• Define and enforce AI-specific security controls, including prompt-injection defenses, agent permission scoping, sandboxing of agent execution environments, and tool/action allowlisting.
• Assess and mitigate secrets-exposure and software supply chain risk introduced by AI-generated code, third-party MCP servers, and agent tool integrations.
• Partner with Security to fold these controls into the CI/CD pipeline and into the monthly Architecture Review Board (ARB) review process.

AI Solution Engineer CI/CD Pipeline & DevSecOps:

• Build out and harden MHA's CI/CD pipelines across GitHub Actions and Azure DevOps/Azure, embedding automated security scanning (SAST/DAST, dependency and secret scanning) and quality gates directly into the pipeline.
• Integrate SonarQube, Playwright, and related tooling so that code quality, test coverage, and security posture are enforced automatically, shifting quality left rather than relying on a separate QA handoff.
• Define and monitor pipeline health metrics (build reliability, deployment frequency, defect escape rate, mean time to remediate) that feed the Operational Excellence and Delivery & Enablement domains.

AI Solution Engineer LLMOps, Observability & Cost Governance:

• Stand up telemetry and tracing for agentic workflows, including token spend, latency, and success/failure rates, to give engineering leadership visibility into agent and model performance.
• Implement token spend and cost monitoring alongside model routing/fallback strategies that balance output quality against cost.
• Feed observability data into scorecards and ARB reviews to support data-driven decisions on tool and model selection.

AI Solution Engineer Cross-Functional Leadership & Enablement:

• Work directly with Digital Solutions Engineering, Data, Analytics & Engineering, IT Operations & Security, and Agile Delivery Management to embed agentic practices, standards, and process improvements across their teams, serving as SME and standard-setter rather than as the delivery owner for their squads' work.
• Develop lightweight internal training, playbooks, and office hours, favoring low-cost channels such as Microsoft Learn, to build engineering-wide fluency in agentic tools.
• Identify and prototype novel, high-leverage applications of AI across Claude, OpenAI, and Azure AI services to accelerate product development and business-facing solutions, prioritizing by measurable impact rather than novelty.
• Act as a thought partner to the Director of Solution Engineering and the SVP Technology on the technical roadmap for the Agentic SDLC program and represent AI engineering considerations in team level planning and Program Increment (PI) execution.

AI Solution Engineer Qualifications:

• 5+ years of professional software engineering experience, including hands-on delivery in cloud-native environments.
• Demonstrated depth in agentic AI development, including prompt engineering, agent orchestration, subagent design, MCP (Model Context Protocol) tool integration, and context engineering, not just familiarity with AI code-completion tools.
• Experience building or applying evaluation frameworks for AI/agent output, such as automated evals, regression testing for agentic changes, or human-in-the-loop (HITL) review processes.
• Working knowledge of AI-specific security practices, including prompt-injection mitigation, agent permission scoping, sandboxing, and tool/action allowlisting.
• Strong working knowledge of Microsoft Azure and the M365 ecosystem, including Azure DevOps or GitHub Actions for CI/CD.
• Proven experience building or hardening CI/CD pipelines with embedded security scanning and automated quality gates (e.g., SAST, OWASP-aligned practices, SonarQube, Playwright or equivalent test automation).
• Solid full-stack engineering foundation, comfortable across application code, infrastructure-as-code, and release automation.
• Strong software engineering fundamentals independent of AI tooling: clean and maintainable code, thoughtful API and system design, robust automated testing (unit, integration, and end-to-end), and disciplined version control and code review practices.
• Experience designing for interoperability and orchestration across distributed systems and APIs, ensuring agents, MCP tools, and enterprise platforms integrate cleanly rather than as isolated point solutions.
• Understanding of data privacy and responsible AI obligations in a regulated, healthcare-adjacent environment (e.g., HIPAA/PHI handling), with the judgment to ensure agentic tools and workflows don't expose protected or sensitive data.