Senior AI Product Engineer

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, and optimize LLM-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, and missing-data handling 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: Identify and drive high-impact opportunities where GenAI, machine learning, and analytics can improve engineering productivity and business outcomes. Minimum Qualifications - 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 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 as LangGraph, Google ADK, AutoGen and evaluation tools like AgentEval. - 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, agentic workflows, and frameworks such as PyTorch or TensorFlow. - Cloud experience with GCP, AWS, or Azure, including deploying ML pipelines in production. - Experience with LLM training, inference, and evaluation workflows, 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, and missing-data treatment. - Strong analytical, problem-solving, and software development skills. - Strong communication skills with the ability to explain technical concepts and findings effectively. - Strong sense of 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 as BigQuery, 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.