ML Ops Engineer — Agentic AI Lab (Founding Team)

Fabrion; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Meaningful equity

Job summary

A pioneering AI company in the San Francisco Bay Area is seeking an ML Ops Engineer to automate model training, deployment, and governance processes. The ideal candidate will have extensive MLOps experience and be proficient in tools like Kubernetes and Terraform. This role offers a competitive salary along with equity opportunities, perfect for those looking to contribute to groundbreaking AI infrastructure.

Qualifications

• 4+ years in MLOps or ML platform engineering.
• Experience with model management tools like MLflow and Weights & Biases.
• Familiarity with large model deployments and tuning libraries.

Responsibilities

• Build and maintain automated pipelines for model training and deployment.
• Manage hybrid compute infrastructure for training and inference.
• Implement and enforce model governance and observability.

Skills

MLOps experience

Model lifecycle management

Container orchestration

Automating pipelines

Infrastructure management

Tools

Kubernetes

Terraform

Docker

Ray

GitHub Actions

Job description

About the Role

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.

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.

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.