Product Development Engineer - Data & AI Scientist

THE ROLE:

We are looking for a practical and experienced Staff Product Development Engineer (Data and AI Scientist) with a strong background in end-to-end development and deployment of AI and machine learning solutions for the semiconductor industry.

The ideal candidate should be comfortable working with large-scale datasets and designing algorithmic and scalable solutions. The candidate should also have excellent interpersonal skills for collaborating with engineering and business teams to deliver impactful outcomes.

• Design, develop, and deploy end-to-end machine learning pipelines—from data ingestion to model serving
• Work with large, complex data sets to extract insights and build predictive or decision-support models
• Implement scalable solutions that integrate with enterprise data and software systems
• Ensure reliability, maintainability, and performance of deployed models in production environments
• Collaborate with cross-functional teams to translate real-world challenges into effective data-driven solutions
• Contribute to internal tooling and frameworks that accelerate AI/ML delivery

• Background in the semiconductor or electronics manufacturing domain
• 7+ years of hands‑on experience in deploying machine learning models and systems in production
• Strong programming skills in Python, with experience in machine learning and/or deep learning libraries such as pandas, numpy, scikit‑learn, PyTorch, or Tensor Flow
• Proven experience working with large‑scale data processing, including distributed data environments (e.g., SQL, Spark, or cloud‑native solutions)
• Solid understanding of software engineering practices—version control, CI/CD, unit testing, containerization, and system monitoring, by using tools like Git Hub, Bitbucket, etc.
• Contributions to open‑source projects or public AI/ML work (e.g., Git Hub, blogs, academic publications)
• Experience with platforms or tools like Snowflake, Databricks, MLflow, Airflow, graph database, LLM and agentic AI framework (e.g., Lang Chain, etc.).
• Familiarity with one or both of the following:
• Large Language Models (LLMs)-based agentic workflow implementations
• Graph-based analytics or knowledge-driven modeling

• Master’s in Computer Engineering, Computer Science, Data Science, or a related technical field