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Fabrion · 5 months ago

ML Ops Engineer — Agentic AI Lab (Founding Team)

San Francisco Bay Area

Full-time

Onsite

Mid, Senior Level

4+ years exp

Fabrion is a pioneering company focused on building intelligent infrastructure through innovative AI solutions. They are seeking an ML Ops Engineer to bridge the gap between ML research and production systems, responsible for automating model training, deployment, and observability pipelines.

Artificial Intelligence (AI)Machine Learning

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)

Qualification

MLOpsModel lifecycle managementKubernetesTerraformPythonCI/CDContainer orchestrationSecurity governanceEmbedding pipelinesSoft skills

Required

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, 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)

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

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