Senior MLOps Engineer

Title: AI/ML Engineer

Duration: Long Term Contract
Location: Cupertino, CA/ Austin, TX (Onsite)
Rate Range: $75/hr. - $65/hr.

Interview mode: Telephonic/Video Conference
Joining: ASAP
This is the description.

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
• gentic 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

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

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.