Company Overview

KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world's leading technology providers to accelerate the delivery of tomorrow's electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.

Group/Division

The Information Technology (IT) group at KLA is involved in every aspect of the global business. IT's mission is to enable business growth and productivity by connecting people, process, and technology. It focuses not only on enhancing the technology that enables our business to thrive but also on how employees use and are empowered by technology. This integrated approach to customer service, creativity and technological excellence enables employee productivity, business analytics, and process excellence.

Job Description/Preferred Qualifications

We are seeking a hands-on AI/ML Engineer specializing in AI agent systems to design and implement agentic workflows that can plan, reason, use tools, and safely execute tasks across complex enterprise environments. You will build orchestration layers, tool interfaces, memory and state management, and evaluation/guardrails to ensure agents are reliable, secure, and production-ready.

Position Overview

We are seeking a hands-on AI/ML Engineer specializing in AI agent systems to design and implement agentic workflows that can plan, reason, use tools, and safely execute tasks across complex enterprise environments. You will build orchestration layers, tool interfaces, memory and state management, and evaluation/guardrails to ensure agents are reliable, secure, and production-ready.

Key Responsibilities

Agent Architecture & Orchestration
Design and implement agent architectures (single-agent and multi-agent) with robust planning, tool use, and state management.
Build orchestration patterns such as supervisor/worker, router-based specialization, and iterative refinement loops.
Develop reusable agent frameworks including prompt templates, tool schemas, and policy-based controls.
Tooling, Integrations & Automation
Create tool interfaces for internal services and data sources (APIs, databases, ticketing, knowledge bases) with strong typing and validation.
Implement safe execution patterns (sandboxing where appropriate, permission gating, step limits, and deterministic fallbacks).
Integrate agents into user-facing and backend workflows (chat, copilots, automation pipelines).
Reliability, Safety & Guardrails
Implement guardrails for tool use, data access, and response policies (PII handling, prompt-injection resistance, output constraints).
Build monitoring for agent behavior: tool-call success rates, failure modes, loops, latency, cost, and user satisfaction signals.
Run incident response and post-mortems for agent failures; improve robustness via systemic fixes and runbooks.
Evaluation & Continuous Improvement
Design evaluation suites for agent behavior (task success rate, tool correctness, factuality/grounding when retrieval is used).
Build regression testing and canary releases to safely ship updates to prompts, tools, and models.
Develop feedback loops using user signals, targeted labeling, and automated test generation for recurring failure patterns.
Performance & Cost Optimization
Optimize agent latency and cost using caching, memoization, selective tool calling, context management, and lightweight models where appropriate.
Implement rate limiting, retries, circuit breakers, and queueing strategies to protect downstream dependencies.
Collaboration & Documentation
Partner with product and engineering teams to translate business workflows into agent designs and measurable success criteria.
Document patterns, best practices, and reference implementations for teams adopting agentic systems.

Required Qualifications

Bachelor's degree in Computer Science, Engineering, Data Science, Human-Computer Interaction, or a related field with 5+ years of relevant experience; OR a Master's/PhD with 3+ years of relevant experience.
Strong programming skills in Python and experience building LLM-powered applications with tool/function calling.
Experience designing APIs/integrations and building secure, maintainable services.
Understanding of reliability engineering concepts (observability, incident response, safe rollouts).
Experience implementing structured outputs (schemas), validation, and error-handling for production systems.
Strong communication and ability to work effectively in cross-functional teams.

Preferred Qualifications

Experience with multi-agent orchestration patterns (supervisor/worker, planner/executor) and stateful workflows.
Experience with prompt injection defenses, safety policies, and data governance for enterprise AI.
Experience with evaluation frameworks for agentic systems (task benchmarks, simulation, golden tasks, human-in-the-loop review).
Experience integrating retrieval (RAG) into agents for grounded reasoning and citations.
Experience with workflow engines/queues (e.g., Airflow, Temporal, Celery) and distributed systems patterns.

What Success Looks Like (First 6-12 Months)

Agent workflows that reliably complete targeted tasks with measurable success metrics and low operational burden.
Safe tool-use and permissioning that prevents unintended actions and protects sensitive systems.
A strong eval + regression pipeline enabling fast iteration without quality regressions.
Improved latency/cost through optimized tool calling, context control, and operational safeguards.

Note: Technology choices may vary by team needs; candidates should be comfortable learning and adapting to new tools.

Minimum Qualifications

Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years

We offer a competitive, family friendly total rewards package. We design our programs to reflect our commitment to an inclusive environment, while ensuring we provide benefits that meet the diverse needs of our employees.

KLA is proud to be an equal opportunity employer

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AI/ML Engineer Agentic AI