Senior MLOps Engineer

Job Title/Role: Senior MLOps Technical Lead Location: 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
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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

Thanks & Regards

Darshan Neema

Client Account Manager, KTEK Resourcing

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A 2277 Plaza Dr. Suite 240, Sugar Land, TX 77479