Fabrion
ML Ops Engineer — Agentic AI Lab (Founding Team)
Fabrion, San Francisco, California, United States, 94199
Overview
If you want to know about the requirements for this role, read on for all the relevant information. 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, 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 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.
#J-18808-Ljbffr
If you want to know about the requirements for this role, read on for all the relevant information. 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, 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 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.
#J-18808-Ljbffr