# ML Ops Engineer — Agentic AI Lab \(Founding Team\) - Company: [Fabrion]() - Location: San Francisco Bay Area - Team: Engineering - Employment type: Full Time - Posted: August 11, 2025 ## Job description **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** * Comfortable with a hybrid team of AI researchers, DevOps, and backend engineers * Interested in aligning ML systems to product delivery, not just papers * Bonus: experience with SOC2, HIPAA, or GovCloud-grade model operations ## **What We’re Looking For** **Experience:** * 5+ years as a full stack or backend engineer * Experience owning and delivering production systems end-to-end * Prior experience with modern frontend frameworks (React, Next.js) * Familiarity with building APIs, databases, cloud infrastructure, or deployment workflows at scale * Comfortable working in early-stage startups or autonomous roles, prior experience as a founder, founding engineer, or a 0-1 pre-seed startup is a big plus **Mindset:** * Comfortable with ambiguity, eager to prototype and iterate quickly * Strong sense of ownership — prefers to build systems rather than wait for tickets * Enjoys thinking about architecture, performance, and tradeoffs at every level * Clear communicator and pragmatic team player * Values equity and impact over prestige or hierarchy * Prior startup or founding team experience ## **Why This Role Matters** Your work will enable models and agents to be trained, evaluated, deployed, and governed at scale — across many tenants, models, and tasks. This is the backbone of a secure, reliable, and scalable AI-native enterprise system. If you dream about using AI to solve some really hard real world problems – we would love to hear from you. ## Apply [Apply on Fabrion]() Canonical job page: