AI Solutions Engineer

Managed Healthcare Associates (MHA); Parsippany-Troy Hills (NJ); On-site; USD 140,000 - 190,000; Full time

Job summary

Managed Health Care Associates, Inc. (MHA) seeks an AI Solution Engineer to lead agentic engineering efforts, building secure CI/CD pipelines, integrating GitHub, VS Code, Copilot, and Claude into an Azure-native stack, and setting standards that squads adopt while they own delivery.

You will drive governance, security, and observability, partner with IT Security and Ops, and evangelize AI tooling across Digital Solutions Engineering and related teams to accelerate product development while

Qualifications

• Experience leading AI-enabled software delivery transformations.
• Ability to design agentic workflows and tool integrations across enterprise systems.
• Proven track record in secure CI/CD and governance for AI/ML pipelines.
• Familiarity with ML/AI safety, privacy, and regulated data handling.

Responsibilities

• Establish and refine the agentic harness integrating GitHub, VS Code, GitHub Copilot, and Claude for AI-assisted development.
• Design agent orchestration patterns, including multi-agent delegation and MCP tool integration.
• Increase agentic code check-in rates and track progress via METL framework.
• Serve as SME on agentic coding practices and evaluate new AI tools for Azure-native environments.
• Design interoperable agentic workflows with Jira, Azure DevOps, and APIs across squads.

Skills

AI engineering

CI/CD design

Context design

Multi-agent systems

Stakeholder collaboration

Security & compliance

Tools

GitHub Copilot

VS Code

Azure DevOps

Jira

SonarQube

Playwright

Job description

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 & 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.

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.

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.

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