Autonomous AI Architect for Smart Manufacturing

Micron Technology, Inc

Singapore

On-site

SGD 180,000 - 260,000

Full time

9 days ago

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Job summary

Micron Technology, Inc. is seeking an ambitious Member of Technical Staff to architect autonomous AI Agents for smart manufacturing, enabling multi-step reasoning and tool execution across complex processes.

The role emphasizes scaling AI agents within CI/CD pipelines to Kubernetes and cloud environments, with heavy focus on performance profiling and secure design. You will collaborate with Data Scientists and Engineers to push the boundaries of agentic AI in Micron’s memory manufacturing

Qualifications

Technical Degree required (Bachelor’s or Master’s in Computer Science, Statistics, or a related field). Ph.D. is a plus.
8+ years of relevant industry experience in data/ML pipelines or production Agentic AI systems.
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 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.

Responsibilities

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 to identify bottlenecks in compute and latency, using profiling tools.
Implement interoperable multi-agent systems using the A2A protocol and 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 and mitigation of prompt-injection risks.
Mentor engineers on agentic system design paradigms and optimization techniques.