MTS, Data Science Agentic Platforms

Own the creation of AI systems optimizing semiconductor design, product engineering, verification & validation, and manufacturing workflows using large-scale data. Architect and implement Agentic AI systems that integrate with Modeling tools, EDA tools, design environments, and product/manufacturing test platforms to automate tasks such as spec translation, design verification, product validation, and test log root cause analysis for components and system-level products. Establish and promote Best Known Methods (BKMs) for deploying LLMs and agentic systems in live environments, ensuring reliability, efficiency, and maintainability. Benchmark and evaluate model performance using structured evaluation frameworks, and continuously refine models through timely tuning, RLHF, and feedback loops. Collaborate with multi-functional teams—including process engineers, design engineers, product and test engineers, and data scientists—to define high-impact problem statements and deliver scalable AI solutions. Communicate technical insights and solution strategies clearly to both technical and non-technical team members through compelling data storytelling and visualizations. Must have a Master's, PhD, or equivalent experience in Electrical Engineering, Computer Science, or a related field. Proficient in Python, with five years of experience working with a variety of semiconductor design & process datasets. Proficient in using enterprise data platforms like Snowflake, BigQuery, MSSQL, Oracle, and AWS Redshift for scalable data processing and analysis. Hands-on experience building and leading AI/ML projects involving LLMs, RAG, and Agentic workflows deployed, demonstrated expertise in ML frameworks such as PyTorch or TensorFlow. Solid understanding of domain-adapted LLM training with practical experience, including pretraining, post training on in-domain synthetic datasets, and model quantization for efficient deployment Must have shown cloud platform experience such as GCP, AWS, or Azure, including deployment of ML pipelines in production environments. Familiarity with LLM evaluation and benchmarking techniques, including RLHF, timely tuning, and reward modeling for hardware-aware use cases. Consistent track record to independently drive AI/ML projects from problem prioritization through deployment in semiconductor environments. Excellent interpersonal experience with the ability to collaborate with process technology, silicon design, design verification, validation, and product engineering teams, and translate sophisticated AI/ML concepts into actionable solutions for hardware development. Experience includes direct application of AI/ML to semiconductor design, processing, workflows. Deep understanding of semiconductor-specific AI/ML applications. Experience with CI/CD pipelines and MLOps practices for ML/LLM deployment. Strong understanding of agentic AI frameworks (e.g., LangGraph, AutoGen) and evaluation tools (e.g., AgentEval). Shown proficiency in translating sophisticated technical concepts into actionable insights for multi-functional teams!