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
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.
