Position Purpose

Leads the design, implementation, and maturity of AI-enabled DevOps platforms, with a focus on Microsoft Copilot, GitHub Copilot, AI agents, and intelligent automation. Partners with technology and business stakeholders to enable scalable adoption of AI-assisted development and automation. Serves as a technical platform leader and advisor, integrating AI tools and agent-based solutions into Azure DevOps workflows and software development practices. Establishes standards, patterns, and best practices to ensure secure, reliable, and governed AI-enabled solutions while accelerating delivery through modern DevOps and continuous improvement.

Core Responsibilities

Note: This is not an all-inclusive listing

• Design, build, deploy, and operate AI agents and intelligent automation solutions, including LLM-powered and multi-agent systems.
• Lead the adoption of Microsoft Copilot, GitHub Copilot, and Copilot Studio to enable AI-assisted development and improve engineering productivity.
• Serve as a technical advisor to DevOps, application, and platform teams on AI-enabled development, automation, and agent-based solutions.
• Design and maintain Azure DevOps CI/CD pipelines supporting AI-assisted development, automated testing, and continuous delivery.
• Partner with software engineering, enterprise architecture, and product teams to integrate AI-driven capabilities into application and platform solutions.
• Architect and manage secure, scalable Azure infrastructure for AI workloads using infrastructure-as-code practices.
• Establish and operationalize DevOps and MLOps practices, including versioning, monitoring, governance, and lifecycle management of AI systems.
• Implement and maintain observability solutions (logging, metrics, tracing, and alerting) for distributed and AI-driven systems.
• Define and enforce security, compliance, and governance standards across AI systems, pipelines, and cloud platforms.
• Evaluate emerging AI and DevOps technologies to improve reliability, developer productivity, and operational efficiency.
• Optimize scalability, performance, and cost for AI-enabled platforms and compute-intensive workloads.

Behavioral Competencies

Note: These are in addition to MGE’s Core Competencies

• Manages Complexity – Navigates sophisticated technical environments and ambiguous AI challenges effectively.
• Drives Results – Consistently delivers high-quality, scalable solutions in fast-paced environments.
• Collaborates – Builds strong partnerships across engineering, data science, and business teams.
• Instills Trust – Gains credibility through technical expertise and reliable execution.
• Strategic Mindset – Anticipates future AI and technology trends and aligns solutions with long-term objectives.

Skills

• Strong experience building, operating, and enabling AI agents, intelligent automation, and AI-assisted development workflows.
• Hands-on experience with Microsoft Copilot, GitHub Copilot, Copilot Studio, or similar AI-assisted engineering tools.
• Deep mastery of Azure DevOps and modern DevOps practices, including CI/CD, Git, GitHub, infrastructure as code, automation, and platform reliability.
• Expert-level experience within the Microsoft ecosystem, including Azure, Azure DevOps, GitHub Enterprise, and Azure-native services.
• Strong understanding of LLM concepts, agent orchestration frameworks, and prompt engineering (e.g., LangChain, AutoGen, CrewAI).
• Experience operationalizing AI and ML systems, including deployment, monitoring, governance, and lifecycle management.
• Expertise in infrastructure as code using Terraform, Bicep, ARM templates, or similar frameworks.
• Advanced experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong programming and automation skills using Python, PowerShell, Bash, or similar languages.
• Experience implementing observability solutions using Azure Monitor, Log Analytics, Application Insights, or equivalent tools.
• Advanced Git usage, branching strategies, and pull request workflows.
• Strong understanding of security, identity, and compliance considerations in cloud and AI-enabled environments.
• Proven ability to lead, enable, and influence DevOps and AI adoption across multiple teams.

Education

Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field required; an equivalent combination of education and experience may be considered.

Relevant certifications preferred, such as:

• Microsoft Certified: Azure DevOps Engineer Expert
• Microsoft Certified: Azure AI Engineer Associate
• Kubernetes certifications (CKA, CKAD)
• Azure or GitHub architecture certifications

Experience

• 7+ years of experience in DevOps, Platform Engineering, Site Reliability Engineering, or related roles supporting complex, cloud-based environments.
• 3+ years of hands-on experience enabling or supporting AI-driven systems, intelligent automation, or AI-assisted development in production environments.

Work Location

• This hybrid role is based at our Madison, WI headquarters. While three days onsite is the minimum, team collaboration and business needs may require additional in-office presence.

• This position may require participation in on-call rotations and occasional off-hours support to ensure platform reliability and availability.

Pre-employment will require satisfactory completion of a background check and drug screen.

We are an AA/EOE employer and consider all qualified candidates without regard to protected status.