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Staff AI Engineer

Micron Boise, ID

Boise, ID

Today

Full time .NET Agentic Ai Ai Agent

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Job Description

Staff AI Engineer at Micron (Boise, ID, onsite) to build modern AI-first full stack applications and production systems, with a focus on custom AI agents, MCP, and operationalizing AI for manufacturing.

Responsibilities

Own features end-to-end across system design, implementation, testing, observability, deployment, and lifecycle support
Support platform enablement and architecture decisions through code reviews and shared engineering standards
Design, build, test, deploy, and operate full stack software solutions
Collaborate with business teams to translate requirements into scalable solutions
Build and implement Custom AI Agents for workflows such as task automation, decision support, summarization, multi-step reasoning, and guided execution
Demonstrate engineering proficiency using Vibe Coding prototypes in a timely development process
Integrate systems using Model Context Protocol (MCP) to enable structured context sharing between models, tools, and agents
Expose tools and capabilities to LLM-based agents and enable interoperability across AI components without hard-coded integrations
Integrate applications with external and internal AI services including LLMs, embeddings, search, and tools via well-designed APIs
Harden AI components for 24x7 critical physical manufacturing systems with focus on reliability, resiliency, latency, performance, security, permissions, data boundaries, observability, and maintainability
Apply retrieval-augmented generation (RAG) systems within production environments
Use AI-assisted development tools (code generation, refactoring, test creation, documentation), and critically evaluate and refine AI-generated output with human judgment
Conduct requirements gathering and analysis for software domains, interfaces, and characteristics
Write code using programming, scripting, and database languages; support testing, deployment, maintenance, and evolution
Follow guidelines for coding standards, code reviews, source control, build processes, testing, and operations

Requirements

Strong full stack software engineering foundation including object-oriented design, clean code, testing strategies, and long-term maintainability
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 across relational and non-relational technologies, including selecting the right tool
Track record of owning software end-to-end from design through lifecycle support, collaborating across 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, including prompt design and tool invocation
Ability to reason about hallucination risk, failure modes, permissions, and guardrails in AI-enabled systems, plus hands-on RAG integration experience

Preferred Qualifications

Experience applying AI-enabled development practices to accelerate engineering workflows and improve productivity
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

Kubernetes / Platform Engineering

Strong interest in Kubernetes and cloud-native infrastructure, including running, upgrading, and troubleshooting production clusters
Desire to deepen expertise in container orchestration, GPU workloads, and platform reliability
Experience designing and running distributed systems on Kubernetes
Understanding of message-driven architectures, horizontal scaling, fault tolerance, and multi-tenant workloads
Help shape a platform roadmap for processing large volumes of data across sites

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

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