ML Ops Engineer — Agentic AI Lab (Founding Team)
Location: San Francisco Bay Area
Type: Full-Time
Compensation: Competitive salary + meaningful equity (founding tier)
Backed by 8VC, we're building a world-class team to tackle one of the industry’s most critical infrastructure problems.
About the Role
Our AI Lab is pioneering the future of intelligent infrastructure through open-source LLMs, agent-native pipelines, retrieval-augmented generation (RAG), and knowledge-graph-grounded models.
We’re hiring an ML Ops Engineer to be the glue between ML research and production systems — responsible for automating the model training, deployment, versioning, and observability pipelines that power our agents and AI data fabric.
You’ll work across compute orchestration, GPU infrastructure, fine-tuned model lifecycle management, model governance, and security e
Responsibilities
- Build and maintain secure, scalable, and automated pipelines for:
- LLM fine-tuning, SFT, LoRA, RLHF, DPO training
- RAG embedding pipelines with dynamic updates
- Model conversion, quantization, and inference rollout
- Manage hybrid compute infrastructure (cloud, on-prem, GPU clusters) for training andinference workloads using Kubernetes, Ray, and Terraform
- Containerize models and agents using Docker, with reproducible builds and CI/CD viaGitHub Actions or ArgoCD
- Implement and enforce model governance: versioning, metadata, lineage, reproducibility,and evaluation capture
- Create and manage evaluation and benchmarking frameworks (e.g. OpenLLM-Evals,RAGAS, LangSmith)
- Integrate with security and access control layers (OPA, ABAC, Keycloak) to enforcemodel policies per tenant
- Instrument observability for model latency, token usage, performance metrics, errortracing, and drift detection
- Support deployment of agentic apps with LangGraph, LangChain, and custom inferencebackends (e.g. vLLM, TGI, Triton)
Desired Experience
Model Infrastructure:
- 4+ years in MLOps, ML platform engineering, or infra-focused ML roles
