Production AI Tech Lead & Data Ops Mentor

KLA; Phoenix (AZ); On-site; USD 112,000 - 190,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Life insurance

401(k) matching

ESPP

Tuition reimbursement

Wellness program

Paid time off

Job summary

KLA in Ann Arbor, MI is seeking an AI Engineer to serve as a technical lead on production AI systems, collaborating with data scientists and full-stack engineers to deliver AI-powered features and robust ML infrastructure.

You will mentor engineers, drive architectural decisions, and help translate experiments into scalable, production-ready solutions, with opportunities for broader leadership over time.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or related field.
• Advanced degree preferred, especially with AI, ML, or distributed systems.

Responsibilities

• Lead design, development, and deployment of production AI systems end-to-end.
• Contribute hands-on to codebase and uphold engineering quality.
• Establish scalable patterns for evaluation, deployment, and reliability.
• Conduct technical design reviews and guide architecture decisions.
• Mentor engineers with regular coaching and guidance.
• Support hiring, including candidate evaluation and onboarding.
• Partner with cross-functional stakeholders to deliver impactful solutions.
• Translate experimentation into stable, production-ready systems.

Skills

AI/ML in prod

LLMs

End-to-end AI

System design

Mentorship

Cross-functional

Hiring support

Education

Bachelor's/Master's in CS/EE/Data Science

Advanced degree preferred

Tools

Model serving

Data pipelines

Evaluation frameworks

Prompt iteration

LLM tooling

Job description

KLA in Ann Arbor, MI is seeking an AI Engineer to serve as a technical lead on production AI systems, collaborating with data scientists and full-stack engineers to deliver AI-powered features and robust ML infrastructure.

You will mentor engineers, drive architectural decisions, and help translate experiments into scalable, production-ready solutions, with opportunities for broader leadership over time.