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

MICRON SEMICONDUCTOR ASIA OPERATIONS PTE LTD; Região Norte; Presencial; BRL 350 000 - 650 000; Tempo integral

Resumo da oferta

MICRON TECHNOLOGY INC. seeks an AI/ML Engineer to drive GenAI-enabled solutions for semiconductor engineering. You will build intelligent systems for data extraction, analytics, and knowledge retrieval, partnering with cross-functional teams to scale production-ready AI workflows.

You will apply Python/SQL, data analytics, and ML techniques to improve quality, cost, and cycle time while deploying in cloud environments and ensuring operational readiness.

Qualificações

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, Data Science, Statistics, Artificial Intelligence, or a related field.
• Minimum 2 years of hands-on experience developing and deploying AI applications in semiconductors, electronics, or other engineering industries.
• Strong programming proficiency in Python and SQL.
• Strong foundation in data analytics and visualization using pandas, scikit-learn, matplotlib, plotly, or similar libraries.
• Familiarity with agentic AI frameworks such as LangGraph, Google ADK, AutoGen and evaluation tools like AgentEval.
• Hands-on experience with LLM-based workflows, RAG, and production-ready ML pipelines.
• Cloud experience with GCP, AWS, or Azure including deploying ML pipelines in production.
• Experience with large, heterogeneous datasets and data cleansing/outlier handling.

Responsabilidades

• GenAI System Development to support semiconductor engineering workflows such as code generation, data extraction, analytics, and knowledge retrieval.
• Develop scalable data pipelines to ingest, clean, transform, and analyze large datasets from manufacturing and engineering systems.
• Apply Python, SQL, and data science libraries to perform deep analysis and deliver engineering insights.
• Build and optimize LLM-based workflows, including prompting, RAG, and benchmarking.
• Develop and productionize ML/DL models for classification, regression, anomaly detection, and decision support.
• Implement robust 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 operationalization of AI/ML solutions in cloud/enterprise environments.
• Communicate technical findings effectively to stakeholders.
• Identify high-impact opportunities where GenAI and analytics improve productivity and outcomes.

Conhecimentos

Python

SQL

Data analytics

LLM workflows

PySpark/BigQuery

Cloud deployment

Python libraries (pandas, scikit-learn

English communication

Formação académica

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

Ferramentas

LangGraph

Google ADK

AutoGen

Descrição da oferta de emprego

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.