AI Integration Specialist

About the Company

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 DevOps, GitHub, 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 DevOps, GitHub, 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 Security, and Customer Success to align AI engineering initiatives with broader Mi-Case AI governance, policies, and strategic objectives.
• Metrics and reporting — define and report on key metrics for AI-assisted development effectiveness (e.g., agent task success rates, developer time saved, defect rates of AI-generated code) and use those metrics to inform continuous improvement.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field — or equivalent demonstrated experience.
• 2+ years of professional software engineering experience, with at least 1–2 years of hands-on work with AI coding agents and AI-assisted development workflows.

Required Skills

• Strong software engineering fundamentals — ability to dive into complex backend code (C# / .NET preferred) and understand it quickly.
• Deep hands-on experience with AI coding agents such as Claude Code, GitHub, Copilot, or similar.
• Demonstrated track record of writing effective agent documentation, custom instructions, skills, or context files that meaningfully improved agent output.
• Experience building developer tools, internal platforms, or infrastructure that other engineers actively used.
• Comfortable working across the stack — CI/CD, cloud infrastructure (Azure preferred), internal tooling, and application code.
• Eligibility to obtain CJIS clearance and pass background checks required for work on U.S. public sector corrections and justice system.
• Proven experience designing and operating agentic development environments and MCP-style integrations, or equivalent custom AI tooling.
• Strong working knowledge of the Microsoft / .NET ecosystem (C#, ASP.NET Core, Blazor, Entity Framework) and Azure DevOps; familiarity with Mi-Case’s stack is a strong plus.
• Experience with containerized or sandboxed development environments at scale (Docker, dev containers, ephemeral cloud environments).
• Understanding of secure software development practices, including secrets management, least-privilege access, and supply chain security — with particular sensitivity to CJIS and public sector regulatory contexts.
• Familiarity with responsible AI principles, including evaluating AI-powered features for accuracy, bias, security, privacy, and regulatory compliance (e.g., NIST AI RMF, EU AI Act awareness).
• Excellent technical writing skills — able to produce documentation that is equally readable by humans and by AI agents.
• Strong client-facing and internal communication skills; ability to translate between engineering, product, security, and executive stakeholders.
• Solid organizational skills, refined attention to detail, and the ability to manage multiple priorities in a fast-paced environment.
• Strong working knowledge of Microsoft applications including Word, Excel, Outlook, and Teams.
• Ability to work calmly under pressure, multitask, and prioritize.

Compensation $100,000- $115,000 Annual

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