About this 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.
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, 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.
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, 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.
Tech stack
LLMKubernetesTerraformDockerAirflowPython
About Fabrion
Fabrion is hiring for the ml ops engineer — agentic ai lab role. NewJob aggregates active openings directly from Fabrion's applicant tracking system, so this listing is current.
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