AI Engineering Tech Lead

About the role

A hands‑on AI Engineering Tech Lead will lead the development and production deployment of AI solutions that improve manufacturing efficiency, quality, and engineering productivity at TSMC Arizona’s advanced semiconductor fabs. Based in Arizona, you will own local delivery while contributing reusable solutions and engineering practices across TSMC’s global manufacturing footprint.

Key Responsibilities

• Lead multiple AI projects from concept to production, translating 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 by working with domain experts on data collection, labeling, model development, evaluation, and integration into engineering workflows and manufacturing systems.
• Own production operations by establishing MLOps/LLMOps practices covering reproducibility, versioning, CI/CD, deployment, monitoring, model drift, incident response, and rollback.
• Build trustworthy applications with appropriate validation, access controls, traceability, and human oversight for systems supporting critical decisions and actions.
• Scale successful solutions by collaborating with global teams to share reusable components and deployment practices, adapting solutions to site‑specific requirements.

Required Qualifications

• 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.
• 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.
• 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 trade‑offs, prioritize competing needs, and collaborate effectively with domain experts and distributed engineering teams.

Preferred Qualifications

• Experience in semiconductor manufacturing, industrial automation, or other demanding production environments.
• Hands‑on experience with LangChain/LangGraph and deep knowledge of agent harness engineering (context and memory management, state persistence, tool execution, retries, human approval flows, guardrails, tracing, automated evaluation).
• Experience with LLM fine‑tuning, LoRA/QLoRA, knowledge distillation, quantization, large‑scale LLM serving (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 (Foundations or Professional level).
• Kaggle Master or Grandmaster recognition.

Benefits

• Medical, dental, and vision plans with flexible options for employees and families.
• 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 to support work‑life balance.