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

Position Overview

We are seeking a hands-on AI/ML Engineer specializing in Retrieval-Augmented Generation (RAG) to design, build, andoptimizeproduction-grade systems that ground LLM responses in enterprise knowledge. You will own end-to-end retrieval pipelines-from ingestion and indexing to hybrid search, reranking, and evaluation-ensuring high relevance, low latency, and measurable reductions in hallucinations and answer failures.

Key Responsibilities

RAG Pipeline Design & Seeing It Through to Production

Design and implement robust RAG pipelines: ingestion, parsing, chunking, enrichment, embedding, indexing, retrieval, reranking, and answer generation.

Choose and tune retrieval strategies (dense, sparse/lexical, and hybrid) to maximize recall and precision for real enterprise queries.

Build citation/grounding mechanisms and response policies to ensure traceable, trustworthy outputs.

Indexing, Search Quality & Ranking

Implement andoptimizevector and hybrid search over structured and unstructured data (documents, wikis, tickets, logs, and metadata).

Develop reranking strategies (cross-encoder, late-interaction, or LLM-based) and fusion methods (RRF/weighted fusion) to improve ranking quality.

Establish query understanding and rewriting techniques (intent classification, expansions, entity/keyword boosting) to improveretrievalrobustness.

Evaluation, Guardrails & Continuous Improvement

Define anevaluationharness for retrieval and generation using offline datasets and online telemetry (precision/recall@k, MRR/nDCG,groundedness).

Implement automated regression tests and quality gates for new prompts, retrievers, and model updates.

Create feedback loops using human review and lightweight labeling to improve relevance over time.

Performance, Reliability & Cost Efficiency

Optimizelatency and throughput using caching, batching, streaming responses, and efficient retrieval/index configurations.

Instrument the full pipeline with logs, metrics, traces, dashboards, andalertingtriage failures with runbooks.

Drive cost-aware design across embedding, retrieval, and generation (token budgets, context windows, adaptive retrieval).

Security, Access Control & Compliance

Implement document-level security and access control in retrieval (ACL-aware indexing, filtering, or query-time authorization checks).

Ensure safe handling of sensitive data, auditability, and compliance with enterprise governance standards.

Collaboration & Enablement

Partner with domain owners and engineering teams to prioritize use cases and integrate RAG into products and workflows.

Document best practices and provide reusable templates for ingestion, evaluation, and deployment.

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 with LLM/RAG development in production environments.

Experience with vector databases or search engines and retrieval concepts (ANN indexes, BM25/lexical search, hybrid retrieval).

Experience designing evaluation methods for retrieval and LLM outputs (grounding, relevance, factuality, and regression testing).

Experience building scalable services and APIs (REST/gRPC), with attention to reliability and performance.

Strong understanding of data processing pipelines, metadata design, and information retrieval fundamentals.

Excellent communication skills and ability to work effectively in cross-functional teams.

Preferred Qualifications

Experience with ranking/reranking techniques (cross-encoders, late-interaction, learning-to-rank) and fusion methods (RRF, weighted scoring).

Experience with document parsing for PDFs/HTML and handling tables, diagrams, or mixed layouts.

Experience with observability and SRE practices for AI systems (SLOs/SLIs, incident response, runbooks).

Experience implementing ACL-aware retrieval and security patterns for enterprise knowledge systems.

Experience building prompt/tooling libraries andmaintainingmulti-model compatibility across LLM providers.

What Success Looks Like (First 6-12 Months)

A standardized RAG pipeline that measurably improves answer relevance while reducing hallucinations and unresolved queries.

A repeatable evaluation framework with quality gates that preventsregressionsduring model/retriever/prompt updates.

Meaningful latency and cost reductions via caching, adaptive retrieval, and efficient indexing/reranking strategies.

Secure, compliant retrieval that enforces access control without sacrificing search quality.

Minimum Qualifications

Doctorate (Academic) or work experience of 0 years , Master's Level Degree or work experience of 2 years , Bachelor's Level Degree or work experience of 3 years

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AI/ML Engineer - RAG