Staff AI DevOps Engineer

Illumina•San Diego, CA

2d•$141,600 - $212,400•Hybrid

About The Position

As a Staff AI DevOps Engineer at Illumina, 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 engineering patterns, and enhance the AI lifecycle—from experimentation, development, evaluation, and deployment through observability, governance, and continuous improvement. The role supports the responsible adoption of reliable, production-ready AI and helps software teams build, deliver, and operate AI-enabled applications at enterprise scale. It also provides technical leadership, cross-team coaching, and mentorship.

Requirements

Typically, at least 8 years of professional software development experience and a bachelor's degree; alternatively, 6 years of experience with a master's degree, or equivalent experience.
Experience with AI/ML infrastructure, agent orchestration, or similar AI-powered development tools.
Proficiency in Python and TypeScript/JavaScript, and experience with AI/ML frameworks and libraries.
Strong understanding of enterprise software architecture patterns and distributed systems design.
Experience with AWS and Azure cloud platforms 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 teams.
Basic understanding of enterprise services such as Microsoft 365 and IAM, and related APIs such as Microsoft Graph API and Azure AD.
Excellent problem-solving skills and ability to work in an innovative, fast-paced incubator environment.

Nice To Haves

Experience in a product- or solutions-oriented role creating tools for internal customers.
Relevant AWS or Azure certifications, or professional AI/ML certifications.
Knowledge of AWS Agent Core, Bedrock, and Lambda, or similar Azure services such as AI Foundry, Agent Loop, and Azure Functions.
Knowledge of MCPs, APIs, and integration patterns for enterprise tools such as ServiceNow, Jira, Confluence, and GitHub.
Experience with AI agent frameworks, LLM integration, or MCP implementations.
Background 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.
Experience managing transitions from traditional to AI-augmented development practices.

Responsibilities

Design and implement AI infrastructure for federated agent development and distributed deployment, streamlining common infrastructure and cloud service requests and support tasks.
Manage and orchestrate AI agents that generate architecturally sound code in accordance with enterprise standards and best practices.
Build and maintain MCP (Model Context Protocol) servers and related infrastructure for agent communication, coordination, and enterprise integration.
Use AI agents to accelerate code reviews through automated analysis, quality checks, and architectural validation.
Integrate AI models and APIs, including LLMs and generative AI, into bot solutions to provide natural language understanding and intelligent responses.
Develop and manage infrastructure as code using tools such as Terraform or Ansible to provision and configure AWS and Azure resources.
Establish monitoring, governance, and security frameworks for enterprise AI agent operations.
Implement and manage secure authentication and authorization for MCP servers, including OAuth flows and API security.
Collaborate with product owners and internal customers to gather requirements and deliver solutions that improve efficiency and user experience.
Work as a forward deployed engineer with functional teams across Infrastructure & Cloud Services, coaching them on AI infrastructure practices and guiding adoption.
Provide technical leadership in transitioning traditional development practices to AI-augmented approaches.
Mentor junior engineers and promote best practices in software development, AI integration, DevOps, cloud, and platform engineering.