Senior DevOps Analyst

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.