Staff/Principal Product Engineer (GenAI, AI/ML & Advanced Data Analytics)

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 160,000; Full time

Job summary

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE. LTD. is seeking a skilled AI/ML engineer to develop GenAI-powered systems for semiconductor engineering.

You will design scalable data pipelines, apply Python/SQL analytics, and build LLM workflows to improve productivity and decision making. You will work with cross-functional teams to deploy AI/ML solutions in cloud environments and communicate findings to both technical and non-technical stakeholders.

Qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, AI, or related field.
• Minimum 2 years of hands-on AI development and deployment in semiconductors, electronics, or engineering contexts.
• Strong Python and SQL programming skills.
• Solid foundation in data analytics and visualization (pandas, scikit-learn, matplotlib, plotly).
• Familiarity with agentic AI frameworks and evaluation tools.
• Experience with modern AI tooling and coding harnesses.
• Hands-on AI/ML systems involving LLMs, RAG, and agentic workflows.
• Cloud experience with GCP/AWS/Azure and production ML pipelines.
• Experience with large, heterogeneous data from multiple systems and data cleansing techniques.
• Strong analytic, problem-solving, and software development capabilities.
• Effective communication and ownership mindset.

Responsibilities

• GenAI system development for engineering workflows like code generation, data extraction, analytics, and knowledge retrieval.
• Develop scalable data pipelines to ingest, transform, and analyze complex manufacturing datasets.
• Apply Python, SQL, and data science libraries to generate insights and visualizations.
• Build, evaluate, and optimize LLM-based workflows including RAG and benchmarking.
• Produce and productionize ML/DL models for classification, anomaly detection, and decision support.
• Implement data processing techniques using distributed frameworks (e.g., PySpark, BigQuery).
• Collaborate with cross-functional teams to translate engineering problems into AI/ML solutions.
• Support deployment, monitoring, and operation of AI/ML solutions in cloud/enterprise environments.
• Communicate technical findings clearly to stakeholders.
• Identify opportunities where GenAI and analytics improve productivity and outcomes.

Skills

Python

SQL

Data analytics

Pandas

scikit-learn

Matplotlib

Plotly

LLM workflows

RAG

Cloud ML deployments

Kubernetes

Communication skills

Ownership and accountability

Education

Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or related field

Tools

Pandas

Plotly

BigQuery

Snowflake

TensorFlow

PyTorch

LangGraph

Google ADK

AutoGen

AgentEval

Job description

Job Description

Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn,communicateand advance faster than ever.

As part of the HIG HBM Product Engineering organization, you will help drive the development of next-generation GenAI, machine learning, and advanced data analytics solutions for semiconductor engineering. In this role, you will work on intelligent systems that improve engineering productivity, strengthen technical decision-making, and unlock insights from complex manufacturing, validation, and engineering workflows.

You will collaborate with cross-functional teams across Product Engineering, DesignEngineering,System Engineering,Data Science, IT, and Manufacturing to prototype, build, and scale practical AI-driven solutions that improve quality, cost, cycle time, and engineering efficiency.

Key Responsibilities

• GenAI System Development:Design, build, and improve GenAI-powered and agentic systems supporting semiconductor engineering workflows such as code generation, data extraction, analytics, documentation automation, failure triage, and technical knowledge retrieval.

• Large-Scale Data Pipelines:Develop scalable data pipelines and analytical workflows to ingest, clean, transform, and analyze large, complex, and heterogeneous datasets from multiple manufacturing and engineering systems.

• Advanced Data Analytics:Apply Python, SQL, and data science libraries (e.g., pandas, matplotlib) to perform deep analysis, generate visualizations, and deliver actionable engineering insights.

• LLM Workflow Engineering:Build, evaluate, andoptimizeLLM-based workflows, including prompting, retrieval-augmented generation (RAG), inference orchestration, benchmarking, and quality evaluation.

• Machine Learning Production:Develop and productionize machine learning and deep learning models for classification, regression, anomaly detection, failure analysis, and engineering decision support.

• Distributed Data Processing:Implement robust data processing techniques such as data cleansing, outlier detection, andmissing-datahandling using distributed or large-scale frameworks (e.g.,PySpark,BigQuery).

• Cross-Functional Collaboration:Partner with domain experts and cross-functional teams to translate complex engineering problems into scalable AI/ML and analytics solutions.

• Production Deployment:Support deployment, monitoring, and operationalization of AI/ML solutions in cloud and enterprise environments.

• Technical Communication:Communicate technical findings, recommendations, and model outcomes clearly to both technical and non-technical stakeholders.

• Innovation Leadership:Identifyand drive high-impact opportunities where GenAI, machine learning, and analytics can improve engineering productivity and business outcomes.

Minimum Qualifications

• Bachelor’sorMaster’s degree inElectricalEngineering, Computer Science, Data Science, Statistics, Artificial Intelligence, ora relatedfield.

• Minimum 2 years of hands-on experience developing and deploying AI applications insemiconductors, electronics, orother engineeringindustries.

• Strong programmingproficiencyin Python and SQL.

• Strong technical foundation in data analytics and visualization, including tools and libraries such as pandas, scikit-learn, matplotlib,plotly, or similar ecosystems.

• Familiarity with agentic AI frameworks such asLangGraph, Google ADK,AutoGenand evaluation tools likeAgentEval.

• Familiarity with modern AI coding tools / agentic coding harnesses, such as Claude Code, Roo Code, Cursor, Cline, Windsurf, Gemini CLI, or similar tools.

• Hands-on experience developing and deploying AI/ML systems involving LLMs, including RAG, agenticworkflows, and frameworks such asPyTorchor TensorFlow.

• Cloud experience with GCP, AWS, or Azure, including deploying ML pipelines in production.

• Experience with LLM training, inference, andevaluationworkflows, including prompt design, benchmarking, validation, or retrieval-augmented systems.

• Experience analyzing large, complex, and heterogeneous datasets from multiple systems and applying sound techniques for data cleansing, outlier handling, andmissing-datatreatment.

• Strong analytical, problem-solving, and software development skills.

• Strong communicationskills with the ability to explain technical concepts and findings effectively.

• Strong senseof ownership, accountability, and engineering rigor.

Preferred Qualifications

• Experience building agentic systems or AI solutions for semiconductor manufacturing, product engineering, validation, yield improvement, reliability, or failure analysis.

• Deep understanding of semiconductor-specific AI/ML applications

• Demonstrated understanding of deep learning architectures and computer vision.

• Experience using enterprise data platforms such asBigQuery, Snowflake,MSSQL, Oracle, or Redshift.

• Experience with Kubernetes or similar production infrastructure and deployment frameworks.

• Experience with web application technologies such as JavaScript, HTML, and CSS.

• Experience designing scalable, enterprise-grade AI/ML systems with attention to reliability, traceability, and operational readiness.

• Familiarity with document-processing pipelines, technical knowledge platforms, or retrieval systems for engineering content.

• Experience working in cross-functional environments spanning engineering, manufacturing, data science, and IT.