ML Ops Engineer — Agentic AI Lab (Founding Team) ML Ops Engineer — Agentic AI Lab (Founding Team)Location: 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.About the RoleOur 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 eResponsibilitiesBuild 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 andinference workloads using Kubernetes, Ray, and TerraformContainerize models and agents using Docker, with reproducible builds and CI/CD viaGitHub 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 enforcemodel policies per tenantInstrument observability for model latency, token usage, performance metrics, errortracing, and drift detectionSupport deployment of agentic apps with LangGraph, LangChain, and custom inferencebackends (e.g. vLLM, TGI, Triton)Desired ExperienceModel Infrastructure:4+ 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 + Infra:Proficient with Terraform, Helm, K8s, and container orchestrationExperience 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 toolsBuilt 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 trailsIntegrated with API rate limits, tenant billing, and SLA observabilityExperience with policy-as-code systems (OPA, Rego) and access layersPreferred 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, optionally Rust or Go for toolingMindset & 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 ForExperience:5+ 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 experience as a founder, founding engineer, or a 0-1 pre-seed startup is a big plusMindset:Comfortable with ambiguity, eager to prototype and iterate quicklyStrong sense of ownership — prefers to build systems rather than wait for ticketsEnjoys thinking about architecture, performance, and tradeoffs at every levelClear communicator and pragmatic team playerValues equity and impact over prestige or hierarchyPrior startup or founding team experienceWhy This Role MattersYour work will enable models and agents to be trained, evaluated, deployed, and governed atscale — 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 hardreal world problems – we would love to hear from you.