Machine Learning Engineer

Job Title/Role: Senior MLOps Technical Lead Location: Cupertino, CA/ Austin, TX Onsite Mandatory Need local Objective: Build intelligent, data-driven platform. The focus is to support the development of next-generation test analytics and test agents that enable faster insights, improved diagnostics, and scalable infrastructure for Generative AI systems connecting test stations, line level data and pipelines . You will build automated evaluation tools, and conduct rigorous statistical analyses to ensure the reliability of both human and AI-based assessment systems. Benchmark, adapt, and integrate AI/ML models into existing software systems. Independently run and analyze ML experiments for real improvements. Must-Have Requirements Requirement Details Backend/Systems Experience 3+ years building production backend or distributed systems (pre-AI experience required) Production AI Systems Has shipped AI/LLM features serving real users at scale — not just prototypes or demos Agentic Systems Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations Python Proficient for backend development Secondary Language Working knowledge of Go, TypeScript, or Rust Cloud Infrastructure Deep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deployment Container & Orchestration Hands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselves LLM Integration Understands token economics, context limits, rate limiting, structured outputs, API failure modes LLM Evaluation Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection) Hands-On Engineer Not just an architect — writes code, debugs production issues, deploys their own work