Senior MLOps Engineer

Role : Senior MLOps Engineer
Cupertino, CA/ Austin, TX Onsite Mandatory
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/Google Cloud Platform/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

Preferred / Differentiators
Built multi-step agentic workflows with tool use and function calling
Experience with agent orchestration frameworks (LangGraph, CrewAI, or custom)
Built guardrails, fallbacks, or graceful degradation for AI systems
Streaming inference and async agent orchestration
Cost/latency optimization: caching, batching, prompt compression
ML observability tools: Langfuse, Arize, Braintrust, W&B
Retrieval systems (vector search, hybrid search) as a tool, not the focus

Screening Questions for Candidates
1. "Describe a production AI agent or skill system you built. What broke and how did you fix it?"
2. "Have you built MCP servers/integrations or custom tool-use systems for LLMs?"
3. "How do you evaluate whether an LLM-based feature is working well? What makes this hard?"
4. "Walk me through how you'd deploy and scale an AI service on Kubernetes."

Not a Fit If
Primarily a model trainer/fine-tuner (we're not training models)
AI experience is mainly academic, research, or tutorial-based
No production systems experience (only notebooks/demos)
Looking for entry-level role with heavy mentorship
Background is primarily data science/analytics rather than engineering
"Architects" who don't write or deploy code themselves