AI Integration Specialist

As the Mi-Case AI Integration Engineer / Specialist, you will accelerate how our engineering and delivery organization works with AI coding tools, AI agents, and AI-assisted workflows. This is a hands-on technical role focused on building infrastructure, tooling, integrations, and documentation that make AI agents dramatically more effective across our codebase, product suite, and customer deployments. AI tools are evolving rapidly, and Mi-Case believes the public sector technology providers that invest in making agents truly productive - not just available - will deliver superior outcomes for our customers and end users.

About the Role

You will own this problem end-to-end: from setting up secure development environments where agents can run safely against our technology stack, to building MCP server integrations that connect agents to our internal systems (such as Azure Dev Ops, Git Hub, and CI/CD pipelines), to creating the documentation, skills, and context that help agents understand how Mi-Case builds software in a CJIS-compliant, regulated environment.

You will have the hands-on technical acumen to do the heavy lifting yourself and the collaboration skills to drive cross-team efforts that raise the bar for AI-assisted processes and workflows across Mi-Case. Reporting to the Director of AI, this role partners closely with Engineering Leadership, and Product Management, and supports both internal business productivity and customer-facing AI capabilities embedded in Mi-Case products.

Responsibilities

• Own the agentic development environment — ensure AI agents can operate in secure, isolated, cloud-based development environments that respect Mi-Case’s CJIS, FedRAMP, and customer data handling requirements; enable agents to execute test suites, run builds, and inspect results safely.
• Build tooling and MCP integrations — design and implement Model Context Protocol (MCP) server integrations that connect AI agents to the systems needed to build, test, debug, and deliver Mi-Case software, including Azure Dev Ops, Git Hub, Jira, CI/CD pipelines, observability tooling, and internal knowledge sources.
• Documentation and context engineering — own the repo-wide AGENTS.md (and equivalent agent guidance files) for Mi-Case codebases; partner with engineering teams to ensure conventions, package structures, architectural patterns, and product domain knowledge are exposed to agents in ways they can effectively consume.
• Skills and agent libraries — build and curate a library of reusable agent skills, prompts, and orchestration patterns aligned to Mi-Case product domains (corrections, jail management, licensing, offender self-service) and to common engineering tasks such as code generation, code review, test authoring, and documentation.
• Developer and delivery enablement — work directly with engineers, QA, implementation consultants, and product managers to identify where AI agents are struggling; address root causes through better documentation, tooling access, prompts, or workflow design; develop low-friction tooling that allows non-engineers (PMs, BAs, designers) to make safe, scoped contributions to Mi-Case products.
• Customer-facing AI feature support — partner with product managers and engineering leads to integrate AI capabilities into customer-facing Mi-Case products (e.g., Customer Portal, Connectivity Assessment Tool, OSSP, OMS workflows), ensuring integrations are secure, observable, and consistent with the responsible AI standards established by the Director of AI.
• Stay on the leading edge — consistently track state-of-the-art AI productivity tooling, agent frameworks, and developer experience trends; pilot promising approaches and drive adoption across Mi-Case where they create measurable value.
• Responsible AI in engineering — implement guardrails, evaluation harnesses, and review processes that ensure AI-generated code and AI-assisted contributions meet Mi-Case’s quality, security, accessibility (Section 508 / WCAG), and regulatory compliance standards before reaching customer environments.
• Cross-functional collaboration — work closely with the Director of AI, Engineering Leadership, Product, Information…