ML Ops Engineer — Agentic AI Lab (Founding Team)

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.