ML Ops Engineer — Agentic AI Lab (Founding Team)

OverviewML Ops Engineer — Agentic AI Lab (Founding Team) at FabrionLocation: San Francisco Bay AreaType: Full-TimeCompensation: 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. 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.ResponsibilitiesBuild and maintain secure, scalable, and automated pipelines for:LLM fine-tuning, SFT, LoRA, RLHF, DPO trainingRAG embedding pipelines with dynamic updatesModel conversion, quantization, and inference rolloutManage hybrid compute infrastructure (cloud, on-prem, GPU clusters) for training and inference workloads using Kubernetes, Ray, and TerraformContainerize models and agents using Docker, with reproducible builds and CI/CD via GitHub Actions or ArgoCDImplement and enforce model governance: versioning, metadata, lineage, reproducibility, and evaluation captureCreate 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 tenantInstrument observability for model latency, token usage, performance metrics, error tracing, and drift detectionSupport deployment of agentic apps with LangGraph, LangChain, and custom inference backends (e.g. vLLM, TGI, Triton)QualificationsModel Infrastructure4+ years in MLOps, ML platform engineering, or infra-focused ML rolesDeep familiarity with model lifecycle management tools: MLflow, Weights & Biases, DVC, HuggingFace HubExperience with large model deployments (open-source LLMs preferred): LLaMA, Mistral, Falcon, MixtralComfortable with tuning libraries (HuggingFace Trainer, DeepSpeed, FSDP, QLoRA)Familiarity with inference serving: vLLM, TGI, Ray Serve, Triton Inference ServerAutomation + InfraProficient with Terraform, Helm, Kubernetes, and container orchestrationExperience with ML CI/CD (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 SupportFamiliarity with LangChain, LangGraph, LlamaIndex or similar RAG/agent orchestration toolsBuilt embedding pipelines for multi-source documents (PDF, JSON, CSV, HTML)Integrated with vector databases (Weaviate, Qdrant, FAISS, Chroma)Security & GovernanceImplemented model-level RBAC, usage tracking, audit trails; API rate limits, tenant billing, and SLA observabilityExperience with policy-as-code systems (OPA, Rego) and access layersPreferred Tech StackLLM Ops: HuggingFace, DeepSpeed, MLflow, Weights & Biases, DVCInfra: Kubernetes (GKE/EKS), Ray, Terraform, Helm, GitHub Actions, ArgoCDServing: vLLM, TGI, Triton, Ray ServePipelines: Prefect, Airflow, DagsterMonitoring: Prometheus, Grafana, OpenTelemetry, LangSmithSecurity: OPA (Rego), Keycloak, VaultLanguages: Python (primary), Bash; Rust or Go for tooling (optional)Mindset & Culture FitBuilder\'s mindset with startup autonomy: you automate what slows you downObsessive about reproducibility, observability, and traceabilityComfortable with a hybrid team of AI researchers, DevOps, and backend engineersInterested in aligning ML systems to product delivery, not just papersBonus: experience with SOC2, HIPAA, or GovCloud-grade model operationsWhat We’re Looking For5+ years as a full stack or backend engineerExperience owning and delivering production systems end-to-endPrior experience with modern frontend frameworks (React, Next.js)Familiarity with building APIs, databases, cloud infrastructure, or deployment workflows at scaleComfortable working in early-stage startups or autonomous roles; prior founder or founding engineer experience is a big plusWhy This Role MattersYour 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.Seniority levelMid-Senior levelEmployment typeFull-timeJob functionEngineering and Information TechnologyIndustries: Technology, Information and Internet #J-18808-Ljbffr