Fabrion
ML Ops Engineer — Agentic AI Lab (Founding Team)
Fabrion, San Francisco, California, United States, 94199
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.
#J-18808-Ljbffr
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.
#J-18808-Ljbffr