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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 and inference workloads using Kubernetes, Ray, and Terraform - Containerize models and agents using Docker, with reproducible builds and CI/CD via GitHub 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 enforce model policies per tenant - Instrument observability for model latency, token usage, performance metrics, error tracing, and drift detection - Support deployment of agentic apps with LangGraph, LangChain, and custom inference backends (e.g. vLLM, TGI, Triton) DESIRED EXPERIENCE Model Infrastructure: - 4+ years in MLOps, ML platform engineering, or infra-focused ML roles - Deep familiarity with model lifecycle management tools: MLflow, Weights & Biases, DVC, - HuggingFace Hub - Experience with large model deployments (open-source LLMs preferred): LLaMA, - Mistral, Falcon, Mixtral - Comfortable with tuning libraries (HuggingFace Trainer, DeepSpeed, FSDP, QLoRA) - Familiarity with inference serving: vLLM, TGI, Ray Serve, Triton Inference Server Automation + Infra: - Proficient with Terraform, Helm, K8s, and container orchestration - Experience with CI/CD for ML (e.g. GitHub Actions + model checkpoints) - Managed hybrid workloads across GPU cloud (Lambda, Modal, HuggingFace Inference, - Sagemaker) - Familiar with cost optimization (spot instance scaling, batch prioritization, model sharding) Agent + Data Pipeline Support:● Familiarity with LangChain, LangGraph, LlamaIndex or similar RAG/agent orchestration tools Built embedding pipelines for multi-source documents (PDF, JSON, CSV, HTML) Integrated with vector databases (Weaviate, Qdrant, FAISS, Chroma) Security & Governance: Implemented model-level RBAC, usage tracking, audit trails Integrated with API rate limits, tenant billing, and SLA observability Experience with policy-as-code systems (OPA, Rego) and access layers Preferred Stack - LLM Ops: HuggingFace, DeepSpeed, MLflow, Weights & Biases, DVC - Infra: Kubernetes (GKE/EKS), Ray, Terraform, Helm, GitHub Actions, ArgoCD - Serving: vLLM, TGI, Triton, Ray Serve - Pipelines: Prefect, Airflow, Dagster - Monitoring: Prometheus, Grafana, OpenTelemetry, LangSmith - Security: OPA (Rego), Keycloak, Vault - Languages: Python (primary), Bash, optionally Rust or Go for tooling Mindset & Culture Fit - Builder's mindset with startup autonomy: you automate what slows you down - Obsessive about reproducibility, observability, and traceability -