About the role
AI summarisedJoin the Smart Manufacturing and AI team at Micron Technology to deliver industry-winning machine learning, custom GenAI, and Agentic AI solutions. You will collaborate with cross-functional teams to build and deploy scalable AI/ML solutions that drive value and insight from Micron’s manufacturing processes and systems.
IDMOnsiteSmart MFG/AI
Key Responsibilities
- Architect and execute large-scale custom model training and fine-tuning jobs (SFT, RLHF) on multi-node, multi-GPU clusters.
- Optimize training throughput and memory efficiency using distributed training strategies (FSDP, DeepSpeed, Megatron-LM) and mixed-precision techniques (FP16/BF16).
- Design and develop autonomous AI Agents capable of multi-step reasoning, planning, and tool execution to automate complex manufacturing workflows.
- Implement Agentic frameworks (e.g., LangChain, LangGraph, CrewAI) to orchestrate LLM interactions with internal APIs, databases, and software tools.
- Profile and debug GPU performance bottlenecks using tools like Nsight Systems or PyTorch Profiler to maximize hardware utilization.
- Build and maintain data/solution pipelines that feed machine learning models and GenAI applications.
- Design and optimize data structures in data management systems (Snowflake, Google Cloud platforms) to enable AI/ML and Agentic solutions.
- Create/Maintain CI/CD pipelines of machine learning and AI Agent solutions in the cloud.
Requirements
- Deep understanding of GPU architecture (memory hierarchy, tensor cores, interconnects like NVLink) and experience managing GPU resources in cloud/on-prem environments.
- Hands-on experience with Distributed Data Parallel (DDP), Fully Sharded Data Parallel (FSDP), and model parallelism techniques.
- Proficiency in fine-tuning Large Language Models using PEFT techniques (LoRA, QLoRA) and optimizing inference engines (vLLM, TensorRT-LLM).
- Experience developing GenAI applications and AI Agents using frameworks like LangChain, LangGraph, LlamaIndex, or AutoGen.
- Proficiency with Large Language Models (LLMs), including prompt engineering, function calling/tool use, and Chain-of-Thought (CoT) reasoning.
- Experience in building and executing end-to-end ML systems automating training, testing and deploying Machine Learning models.
- Familiarity with machine learning frameworks (PyTorch is required, TensorFlow, scikit-learn, etc.).
- Strong scripting and programming skills in Python or Java (Python preferred).
- Experience with continuous integration/continuous delivery (CI/CD) tools (Jenkins, Git, Docker, Kubernetes).