Staff DevOps Engineer

Location San Diego, CA hybrid. Not remote .

Position Summary As a Staff DevOps Engineer, you will help shape how AI transforms software development and infrastructure services across the organization. As a key technical member of the DevOps team, you will design and build secure, scalable, enterprise-grade AI solutions; including infrastructure for federated AI agents, MCP servers, and AI-augmented development workflows across AWS and Azure.

You will work hands-on with teams across Infrastructure & Cloud Services and software development to solve complex technical challenges, establish sound engineering patterns, and enhance the end-to-end AI lifecycle; from experimentation, development, evaluation, and deployment to observability, governance, and continuous improvement. Through this work, you will accelerate the responsible adoption of reliable, production-ready AI solutions and improve how software teams build, deliver, and operate AI-enabled applications at enterprise scale.

• Design and implement AI infrastructure to support federated agent development and distributed deployment across the organization to streamline common infrastructure and cloud service requests and support tasks.
• Manage and orchestrate AI agents to generate architecturally sound code, ensuring adherence to enterprise standards and best practices.
• Build and maintain MCP (Model Context Protocol) servers and related infrastructure for agent communication, coordination, and enterprise integration.
• Accelerate code review processes by leveraging AI agents for automated analysis, quality checks, and architectural validation.
• Integrate AI models and APIs, including LLMs and generative AI, into the bot solutions to provide natural language understanding and intelligent responses.
• Develop and manage infrastructure-as-code (IaC) using tools like Terraform or Ansible to provision and configure resources on AWS, Azure.
• Establish robust monitoring, governance, and security frameworks for AI agent operations in enterprise environments.
• Implement and manage secure authentication and authorization systems for MCP servers, including OAuth flows and API security.
• Collaborate closely with product owners and internal customers to gather requirements and deliver solutions that improve efficiency and user experience.
• Act as a forward deployed engineer, working with different functional teams across the Infrastructure & Cloud Services department to coach on AI infrastructure best practices and guide adoption.
• Provide technical leadership in transitioning traditional development practices to AI-augmented approaches
• Mentor and guide junior engineers, promoting best practices in software development, AI integration, DevOps, Cloud and Platform Engineering.
• Listed responsibilities are an essential, but not exhaustive list, of the usual duties associated with the position. Changes to individual responsibilities may occur due to business needs.
• Typically requires a minimum of 8 years of professional software development experience with a demonstrated ability to architect and build scalable systems with a bachelor's degree; or 6 years with a master's degree; or equivalent experience.
• Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.
• Proficiency in Python, TypeScript/JavaScript, and experience with AI/ML frameworks and libraries.
• Strong understanding of enterprise software architecture patterns and distributed systems design.
• Experience with cloud platforms (AWS, Azure) and infrastructure-as-code tools.
• Experience with Docker, Kubernetes, and container orchestration in production environments.
• Strong understanding of information security principles, OAuth 2.0/OpenID Connect flows, API security, and secure system design.
• Strong communication and mentoring skills with the ability to collaborate across different teams.
• Basic understanding of enterprise services like Microsoft 365 and IAM (Identity and Access Management) and their APIs (e.g., Microsoft Graph API, Azure AD).
• Excellent problem-solving skills and the ability to work in an innovative, fast-paced incubator environment.
• Demonstrated experience in a product or solutions-oriented role, creating tools for internal customers.
• Relevant certifications from AWS, Azure, or professional certifications in AI/ML are highly desirable.
• Knowledge of AWS services such as Agent Core, Bedrock, and Lambda, or similar services on Azure including AI Foundry, Agent Loop, and Azure Functions is a significant plus.
• Knowledge of MCPs/APIs and integration patterns for common enterprise tools like ServiceNow, Jira, Confluence, GitHub, etc., is a plus.
• Experience with AI agent frameworks, LLM integration, or Model Context Protocol (MCP) implementations.
• Background in building federated systems or multi-agent AI platforms.
• Experience with AI governance, security, and compliance in enterprise environments.
• Familiarity with code generation tools, automated testing frameworks, and AI-powered development workflows.
• Knowledge of vector databases, embedding systems, and retrieval-augmented generation (RAG) architectures.
• Previous experience managing transitions from traditional to AI-augmented development practices.

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