TSMC Arizona - AI Engineering Tech Lead

TSMC • USA-Arizona, AZ

6d

About The Position

TSMC Arizona is seeking a hands-on AI Engineering Tech Lead to lead the development and production deployment of AI solutions that improve manufacturing efficiency, quality, and engineering productivity. The role partners with fab domain experts to turn complex operational challenges into reliable, scalable applications, selecting suitable approaches across areas such as computer vision, anomaly detection, predictive analytics, robotics, reinforcement learning, generative AI, and agentic AI.

Based in Arizona, the role owns local delivery while contributing reusable solutions and engineering practices across TSMC’s global manufacturing footprint. The Tech Lead provides technical direction and mentorship across multiple application projects, remains actively involved in design and development, and establishes production operations and trustworthy application practices.

Requirements

• Master’s degree in AI/ML, Computer Science, Industrial Engineering, Computer Engineering, Electrical Engineering, or a related technical discipline.
• At least 5 years of professional experience developing AI/ML applications, including substantial hands-on responsibility for production deployment and ongoing operations.
• Proven experience leading teams of at least 7 engineers, typically 7–10 or more, across multiple AI application projects.
• Demonstrated record of taking AI solutions beyond prototypes into sustained production use, with measurable improvements in quality, productivity, reliability, or cost.
• Strong applied expertise in one or more areas such as computer vision, anomaly detection, time-series modeling, predictive analytics, robotics, reinforcement learning, or generative AI, and ability to guide work across adjacent domains.
• Strong Python and software engineering skills, including experience with PyTorch or comparable ML frameworks, data pipelines, APIs, automated testing, and performance optimization.
• Practical experience with containerization, Kubernetes, MLOps, and CI/CD, as well as production monitoring and troubleshooting.
• Familiarity with Agile and Spec-Driven Development (SDD), including clear requirements, testable acceptance criteria, and disciplined release practices.
• Ability to communicate technical tradeoffs, prioritize competing needs, and collaborate effectively with domain experts and distributed engineering teams.
• Ability to perform the essential functions of the job with or without reasonable accommodation.

Nice To Haves

• Experience in semiconductor manufacturing, industrial automation, or other demanding production environments.
• Hands-on experience with LangChain/LangGraph and deep knowledge of agent harness engineering, including context and memory management, state persistence and checkpointing, tool execution, retries and recovery, human approval flows, guardrails, tracing, and automated evaluation.
• Experience with LLM fine-tuning, LoRA/QLoRA, knowledge distillation, quantization, large-scale LLM serving with vLLM/SGLang, RAG/KG-RAG, or multi-agent systems, including reusable Agent Skills.
• Experience with deployment and observability tools such as Azure DevOps, Argo CD, Harbor, Prometheus, Grafana, ELK Stack, and OpenTelemetry.
• Experience deploying vision or robotics applications on edge devices, or transferring reinforcement learning solutions from simulation into operational environments.
• Experience scaling AI applications across multiple physical sites.
• Anthropic Claude Certified Architect certification at the Foundations or Professional level.
• Kaggle Master or Grandmaster recognition.

Responsibilities

• Lead multiple AI projects from concept through production, translating manufacturing needs into technical specifications, delivery plans, and measurable business outcomes.
• Design architectures, develop and review code, guide experimentation, troubleshoot failures, and resolve performance and reliability issues.
• Lead and mentor engineers, set technical direction, manage dependencies, and establish engineering standards across concurrent application projects.
• Develop practical AI/ML solutions with domain experts, including data collection, labeling, model development and evaluation, and integration into engineering workflows and manufacturing systems.
• Establish MLOps/LLMOps practices for reproducibility, versioning, CI/CD, deployment, monitoring, model drift, incident response, and rollback.
• Implement appropriate validation, access controls, traceability, and human oversight for applications supporting critical decisions and actions.
• Collaborate with global teams to share reusable components and deployment practices and adapt solutions to site-specific requirements.

Benefits

• Medical, dental, and vision plans.
• Income-protection programs providing financial assistance during injury or illness.
• 401(k) Retirement Savings Plan with competitive employer contributions.
• Paid time-off programs and holidays.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior