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.
Job Description/Preferred Qualifications
The AI Software Engineer will design and build AI-driven applications powered by large language models (LLMs), with a focus on integrating AI capabilities into user-facing products. This role emphasizes application development, orchestration, and user experience rather than model training. The engineer will develop scalable backend services, prototype intuitive user interfaces, and enable seamless interaction between users and LLM-based systems.
This position requires a strong blend of software engineering, systems thinking, and practical AI implementation to deliver production-ready solutions that leverage LLM APIs and frameworks.Design and develop end-to-end AI applications that leverage LLMs for business use cases
Build and maintain backend services that orchestrate LLM interactions, prompts, and workflows
Prototype and develop intuitive user interfaces that enable effective human-AI interaction
Integrate LLM APIs (e.g., OpenAI, Azure OpenAI, Anthropic) into scalable applications
Develop prompt engineering strategies and manage prompt lifecycle for performance and reliability
Implement retrieval-augmented generation (RAG) architectures using internal and external data sources
Design data pipelines and connectors to enterprise systems (e.g., databases, APIs, knowledge bases)
Monitor, evaluate, and improve AI application performance using metrics such as latency, cost, and response quality
Collaborate with product managers, data scientists, and business stakeholders to translate requirements into AI-enabled solutions
Ensure responsible AI practices, including privacy, security, and bias mitigation
Rapidly prototype AI tools and iterate based on user feedback