AI Engineering Tech Lead

TSMC•Phoenix, AZ

1d•Onsite

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. You will partner with fab domain experts to turn complex operational challenges into reliable, scalable applications. Your work may span computer vision, anomaly detection, predictive analytics, robotics, reinforcement learning, generative AI and agentic AI, selecting the right approach for each problem. Based in Arizona, you will own local delivery while contributing reusable solutions and engineering practices across TSMC’s global manufacturing footprint.

Requirements

Master’s degree in AI/ML, Computer Science, Industrial Engineering, Computer Engineering, Electrical Engineering, or a related technical discipline.
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.
A 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, with the 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, plus 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.

Nice To Haves

Experience in semiconductor manufacturing, industrial automation, or other demanding production environments.
Hands-on experience with LangChain/LangGraph, supported by deep knowledge of agent harness engineering: 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 is also a plus.

Responsibilities

Lead multiple AI projects from concept to production. Translate manufacturing needs into technical specifications, delivery plans, and measurable business outcomes.
Stay hands-on. 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. Work with domain experts on data collection, labeling, model development, evaluation, and integration into engineering workflows and manufacturing systems.
Own production operations. Establish MLOps/LLMOps practices covering reproducibility, versioning, CI/CD, deployment, monitoring, model drift, incident response, and rollback.
Build trustworthy applications. Implement appropriate validation, access controls, traceability, and human oversight for systems supporting critical decisions and actions.
Scale successful solutions. Collaborate with global teams to share reusable components and deployment practices, adapting solutions to site-specific requirements.