About MLOPS

Senior engineers.
Production AI.
Since 2018.

We're a focused team of ML platform engineers who've spent careers shipping models — at hyperscalers, fintechs, and AI labs. We started MLOPS in 2018 because the gap between research and production kept eating teams alive. It still does. We close it.

Our mission

Make production AI boring.

"Boring" is the highest praise in infrastructure. It means predictable. Reproducible. Understood. Affordable. Auditable. The opposite of how most ML systems feel today.

We bring the discipline of platform engineering — internal developer platforms, golden paths, error budgets, GitOps — to ML and LLM systems. Then we hand the keys over.

  • ✓
    No vendor lock-inOpen standards (Iceberg, MLflow, OpenTelemetry, MCP). You own your stack on day one and on day 1,000.
  • ✓
    No offshore handoffsEvery engagement is staffed and led by senior engineers in our home time zones.
  • ✓
    No retainer farmingWe design ourselves out of the picture once you're stable. Knowledge transfer is the deliverable.
  • ✓
    No theatreWorking code over slides. Architecture diagrams in production, not in pitch decks.
How we work

Six operating principles
we won't compromise on.

01

Outcomes over outputs

If we don't know what success looks like in numbers, we don't start. Every engagement begins with a written ROI model.

02

Smallest possible architecture

We strip every component that isn't earning its complexity. Less surface area means fewer 3am pages.

03

Open standards by default

Iceberg over proprietary formats. MLflow over closed registries. OpenTelemetry over vendor SDKs.

04

Tests, evals, and SLOs

Every piece of production code has tests. Every model has evals. Every endpoint has an SLO. Day one.

05

Document everything

Runbooks, ADRs, model cards, dashboards. We leave behind a stack the next engineer can run alone.

06

Security is not a feature

Threat modeling, scoped credentials, signed artifacts, audit trails. From day one, not bolted on for SOC 2.

Team

A small team of specialists.

We stay small on purpose. Every engineer has shipped a production ML or LLM system. Every engagement is led by one of us, not a junior with a slide deck.

JD

Founder · ML Platform

Previously: lead infrastructure engineer at a hyperscaler. 12 years shipping ML platforms.

Kubernetes · MLflow · Ray · vLLM
RK

LLMOps lead

Previously: senior research engineer at an AI lab. Built RAG and eval systems for 100M+ users.

LangSmith · vLLM · Pinecone · DSPy
SP

Reliability & Cost lead

Previously: SRE at a top-5 fintech. Cut a $40M annual cloud bill in half over 18 months.

FinOps · SLOs · Argo · Karpenter
AT

Data & Governance lead

Previously: data platform lead at a healthcare unicorn. HIPAA, SOC 2, EU AI Act in production.

Iceberg · dbt · Tecton · Unity Catalog

Plus 8 senior engineers across MLOps, LLMOps, agents, and SRE. We're hiring.

Want to know if we're the right fit?

Best way: a 30-minute conversation. We'll give you our honest read on your stack — even if the answer is "you don't need us yet."

Book a discovery call →