Staff AI Engineer

Micron is hiring a Staff AI Engineer in Boise, ID (onsite) to help build modern AI-first full stack applications and production systems. In this role, you will focus on custom AI agents, Model Context Protocol (MCP), scalable engineering practices, and operationalizing AI capabilities such as retrieval-augmented generation (RAG) in critical manufacturing environments.

What you will do

• Contribute to platform enablement through architecture decisions, code reviews, and shared engineering standards while designing, building, testing, deploying, and operating full stack software solutions end-to-end.
• Own features across the full lifecycle, including system design, implementation, testing, observability, and ongoing lifecycle support, and collaborate with business teams to translate requirements into scalable solutions.
• Build and implement Custom AI Agents to support workflows such as task automation, decision support, summarization, multi-step reasoning, and guided execution, including proficiency with timely prototyping and Vibe Coding.
• Integrate and apply Model Context Protocol (MCP) so models, tools, and agents can share structured context, expose tools and capabilities to LLM-based agents, and interoperate across AI components without hard-coded integrations.
• Integrate applications with external and internal AI services (LLMs, embeddings, search, and tools) through well-designed APIs and harden AI components for 24x7 critical physical manufacturing systems with attention to reliability, resiliency, latency, performance, security, permissions, data boundaries, observability, and maintainability, including RAG systems.
• Use AI-assisted development tools (code generation, refactoring, test creation, documentation), critically evaluate AI-generated output, and apply human judgment to architecture, correctness, and long-term maintainability.
• Perform requirements gathering and analysis to understand software domains, interfaces, and characteristics; write code using programming, scripting, and database languages; support testing, deployment, maintenance, and evolution; and follow guidelines covering coding standards, code reviews, source control, build processes, testing, and operations.

Required qualifications

• Strong full stack software engineering fundamentals, including object-oriented design, clean code practices, testing strategies, and long-term maintainability.
• Proven experience building and operating production-grade backend or full stack systems, including API design, service integration, and data persistence.
• Ability to design and evolve scalable system architectures with tradeoffs across performance, reliability, security, and developer productivity.
• Experience in cloud-native environments, including CI/CD pipelines, containerized deployments, and modern operational practices.
• Solid understanding of data modeling and storage technologies (relational and non-relational) and selecting the right tool for the problem.
• Track record of owning software end-to-end from design through lifecycle support and collaborating with engineering and business teams.
• Experience with C#, ASP.NET, Angular, RESTful APIs, SQL and NoSQL databases, and Kubernetes-based platforms.
• Experience designing and implementing Custom AI agents beyond simple chat interfaces.
• AI-native skills including prompt engineering, Vibe Coding, context window management, and chain-of-thought design.
• Experience integrating systems using MCP or equivalent structured agent/tool interfaces, with strong understanding of prompt design and tool invocation.
• Ability to reason about hallucination risk, failure modes, permissions, and guardrails in AI-enabled systems, including hands-on experience integrating RAG-based systems.

Technologies

• C#, ASP.NET, Angular
• RESTful APIs
• SQL, NoSQL
• Kubernetes
• CI/CD pipelines, containerized deployments
• Model Context Protocol (MCP), LLMs, embeddings, search, retrieval-augmented generation (RAG)
• Prompt engineering, Vibe Coding, context window management, chain-of-thought design

Kubernetes and platform engineering focus

• Strong interest in Kubernetes and cloud-native infrastructure, including running, upgrading, and troubleshooting production clusters and deepening expertise in container orchestration, GPU workloads, and platform reliability.
• Experience designing and running distributed systems on Kubernetes, including message-driven architectures, horizontal scaling, fault tolerance, and multi-tenant workloads, and contributing to a roadmap for a platform that processes large volumes of data across sites.

Preferred qualifications

• Experience applying AI-enabled development practices to accelerate engineering workflows and improve productivity.
• Demonstrated ability to critically evaluate AI-generated outputs and refine them for production-grade systems.
• Experience building interoperable AI systems without hard-coded integrations.
• Strong understanding of operationalizing AI in critical environments with high reliability and performance requirements.