AI/ML Engineer - MLOps/SRE

Job description

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 MLOps and Site Reliability Engineering (SRE) to build, operate, and continuously improve production-grade machine learning systems. In this role, you will partner with data scientists, data engineers, and software teams to standardize the ML lifecycle, improve reliability and performance, and enable rapid, safe delivery of models and AI services at scale.  Key Responsibilities  • Production ML Platform & Tooling  • Design and implement reusable MLOps platform capabilities for training, deployment, and monitoring of ML/LLM systems.  • Build standardized pipelines for data validation, feature generation, training, evaluation, model packaging, and release.  • Own model registry, artifact storage, and metadata lineage to ensure reproducibility and auditability.  • Deployment Engineering & Release Safety  • Deploy models and AI services using containers and orchestration (e.g., Kubernetes) with robust rollout strategies (blue/green, canary, A/B).  • Create CI/CD workflows for ML code and pipelines, including automated tests, quality gates, and approval controls.  • Harden inference services for low latency and high throughput using caching, batching, autoscaling, and efficient model serving patterns.  • Reliability, Observability & Incident Response (SRE)  • Define and track service-level indicators (SLIs) and service-level objectives (SLOs) for ML services, pipelines, and data dependencies.  • Implement end-to-end observability: structured logging, metrics, tracing, dashboards, and alerting for both infrastructure and model behavior.  • Lead incident response and post-incident reviews; drive systemic fixes through runbooks, automation, and reliability engineering practices.  • Model & Data Monitoring  • Implement monitoring for model quality and data health: drift, bias, performance degradation, and data pipeline anomalies.  • Build automated feedback loops to trigger investigations, retraining workflows, and safe rollback when quality thresholds are breached.  • Security, Compliance & Governance  • Integrate security best practices: secrets management, least-privilege access (RBAC), network controls, and vulnerability scanning.  • Support compliance and governance requir