← All roles

ML Ops Engineer — Agentic AI Lab (Founding Team)

Fabrion· Engineering
Apply on company site
San Francisco Bay AreaFull-timePosted 11mo ago

About the role

ML Ops Engineer to bridge ML research and production systems, automating model training, deployment, versioning, and observability pipelines for agents and AI data fabric in an open-source LLM/infrastructure context.

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)

Qualifications

  • 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
  • Proficient with Terraform, Helm, Kubernetes, 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 instances, batch prioritization, model sharding)
  • 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)
  • 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

Skills

Required:ML lifecycle managementMLOps toolingLarge model deploymentInference serving

Benefits

  • Competitive salary + meaningful equity (founding tier)
  • Backed by 8VC
  • Hybrid/On-site collaboration in a founder team environment
Last updated Sep 9, 2026. Interested? Apply on the company site.

About Fabrion

AI-native platform purpose-built for the new industrial era

AI-native platform purpose-built for the new industrial era

Company profileWebsite

More open roles at Fabrion