Member of Technical Staff - Agentic AI Architect

Job Summary – Member of Technical Staff - Agentic AI Architect, Smart Manufacturing AI Pathfinding & Innovation (AIPI)

Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The memory solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.

The Smart Manufacturing and AI team at Micron Technology is looking for an ambitious engineer (Member of Technical Staff | MTS) to deliver industry-winning Agentic AI solutions that power Micron’s dominance in the highly competitive memory solutions market. This role sits at the intersection of applied Agentic AI engineering and production systems work: you will architect autonomous AI Agents and the agentic and data pipelines that power them, build robust CI/CD deployment paths to Kubernetes and containers, and push the limits of agent performance profiling and evaluation to turn massive data and compute into measurable manufacturing impact. Qualified applicants will have experience across data/cloud technologies and systems engineering, with extensive practice modeling data, querying, and deploying scalable pipelines to execute AI agents. You will collaborate with Data Scientists, Data Engineers and expert users to build and deploy scalable Agentic AI solutions that drive value and insight from Micron’s manufacturing processes and systems.

If you enjoy solving hard problems at scale, thrive on turning speculative research into production-grade systems, and want to see your work drive silicon to market, this is the team for you.

Responsibilities include, but not limited to:

Design and develop autonomous AI Agents capable of multi-step reasoning, planning, and tool execution to automate complex manufacturing workflows.

Implement Agentic frameworks to orchestrate LLM interactions with internal APIs, databases, and software tools.

Build and maintain agent evaluation harnesses to benchmark agent accuracy, reliability, and task success across complex, multi-step workflows.

Analyze and profile agent and inference workloads (e.g., tool-calling latency, multi-agent orchestration) to identify and resolve bottlenecks in compute and latency, using agent observability and profiling tools (e.g., PyTorch Profiler, LangSmith, OpenTelemetry).

Implement interoperable multi-agent systems using the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), including MCP tools, plugins, and skills.

Create/maintain CI/CD pipelines for deploying AI Agents to Kubernetes and containerized environments in the cloud.

Design and implement security controls for autonomous AI Agents, including sandboxing, access control, and mitigation of prompt-injection and tool-misuse risks.

Mentor engineers on agentic system design paradigms and optimization techniques.

Education Qualifications:

Technical Degree required (Bachelor’s or Master’s in Computer Science, Statistics, or a related field). Ph.D. is a plus.

Minimum Qualifications:

Experience optimizing inference engines (vLLM, TensorRT-LLM) for autonomous AI Agent and LLM workloads.

Experience developing GenAI applications and AI Agents using one or more frameworks. Microsoft Agent Framework or Google Agent Development Kit preferred.

Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain-of-Thought (CoT) reasoning.

Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, and vector search.

Experience building and operating end-to-end systems that automate the testing, evaluation, and deployment of autonomous AI Agents.

Familiarity with MCP (Model Context Protocol) tools, plugins, and skills, and the Agent-to-Agent (A2A) protocol for building interoperable multi-agent systems.

Understanding of AI Agent security practices, including sandboxing, access control, and mitigating prompt-injection and tool-misuse risks.

Familiarity with machine learning frameworks (PyTorch, TensorFlow, scikit-learn, etc).

Strong scripting and programming skills, with software development skills and a desire to work in a cloud environment.

8+ years of relevant industry experience, spanning large-scale data/ML pipelines and/or production Agentic AI systems.

Outstanding analytical thinking, interpersonal, oral, and written communication skills.

Ability to prioritize and meet critical project timelines in a fast-paced environment.

Experience with Kubernetes for orchestrating containerized applications, including deployment and scaling of AI Agent workloads.

Preferred Qualifications:

Experience designing and coordinating Multi-Agent Systems, including collaboration between specialized agents.

Experience designing and operating agent swarms for distributed, parallelized task execution.

Experience with agent profiling and evaluation tooling to identify performance regressions and bottlenecks across agent pipelines.

Hands-on experience with LangChain, LangGraph, or CrewAI.

Proficiency in TypeScript and/or Rust.

Demonstrated ability to study and transform data science prototypes into production solutions.

Knowledge of computer vision and/or signal processing, including techniques for classification and feature extraction.