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# Engineer, Agentic Solutions

- **Company:** Micron Technology, Inc.
- **Location:** San Jose, CA, United States
- **Salary:** $90,000.0 - $192,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Databases, Data Cleansing, Python (Programming Language), Performance Tuning, Systems Integration, Supervised Learning, Large Language Models, Multi-Agent Systems, Model Validation, Information Technology
- **Published:** August 4, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=57b484c8914f766f

## About the Role

* B.Tech / M.S / M.Tech in Data Science, Statistics, AI, Mathematics, Computer Science, Electrical, ECE, Engineering Physics, Mechanical, Chemical, Metallurgy, or Material Science.
 * Strong foundation in Python and modern ML/LLM toolchains, with comfort working with engineering/manufacturing datasets.
 * Understanding of ML fundamentals (supervised, unsupervised, and semi-supervised learning), data preparation, and basic model evaluation.
 * Ability to extract and prepare data from common enterprise sources.
 * Clear written and verbal communication, with the ability to collaborate across levels and disciplines., * Exposure to LLM and RAG concepts with a demonstrated interest in agentic workflows.
 * Hands-on academic or project work building agents or LLM-powered applications.
 * Practical experience with agent frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
 * Familiarity with prompt tuning, evaluation loops, or RLHF techniques.
 * Experience integrating with APIs, databases, or EDA/modeling environments.

## Description

* Prototype agentic workflows that support engineering use cases (e.g., spec interpretation, design/verification support, test-log triage) using modern agent frameworks and structured evaluation loops.
 * Build intelligent agents with planning, tool-use, memory, and reflection capabilities using frameworks such as LangChain, LangGraph, CrewAI, or AutoGen.
 * Integrate agents with enterprise tools (APIs, databases, EDA/modeling environments, and internal platforms) for end-to-end task execution.
 * Participate in model evaluation (prompt-level and system-level) and iterate using feedback loops (e.g., prompt tuning/RLHF where appropriate).
 * Collaborate cross-functionally with process/design engineers, product/test engineering, and data scientists to translate problem statements into working prototypes.


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