Mirantis k0rdent AI Joins NVIDIA-Certified Hypervisors Program. READ NOW

We embed with your team until your AI runs in production

Mirantis Forward Deployed Engineers sit with your infrastructure, data and security teams, write the code your environment needs, and stay accountable for a working outcome: a GPU factory serving tenants, inference and agents in daily use, regulated workloads cleared for go-live, legacy platforms moved onto something your team can run.

SCOPE AN ENGAGEMENT

SEE HOW IT WORKS

How They Work

With your team, in your channels, on your backlog.

What They Own

A working outcome in production, agreed on day one.

Who Backs Them

The engineers who build Mirantis solutions, including k0rdent, MOSK, and MKE.

How It Ends

Your team runs it. No dependency left behind.

THE LAST MILE

The last mile is where AI programs stall.

Between a working reference architecture and a service the business can depend on sits a set of problems that belong only to you: your network, your identity stack, your compliance regime, the platforms you already run, the data you cannot move. Closing that gap takes engineers working inside your environment, alongside your own.

Capacity racked and waiting

Capacity sits idle while drivers, fabric, scheduling and multi-tenancy get worked out one ticket at a time.

Pilots that never reach production

A model or an agent works in a notebook; security review, observability and day 2 operations were never scoped.

Integrators who leave with the knowledge

The build finishes, the consultants move on, and the platform becomes technical debt – something nobody on staff can change.

How it works

Embed. Build. Ship. Hand off.

Every engagement is anchored to an outcome we define together day one — an agentic solution deployed, a model in production, a GPU cluster serving tenants, a regulated workload cleared for go-live — and measured against it.

Embed

A small pod of engineers and strategists joins your team, gets access to your environment and learns the real constraints — network, identity, policy, data gravity.

Build

Engineers write and ship the integration code, templates and automation your environment needs, in short cycles, in your repos.

Ship

The outcome goes live with production guardrails: observability, security review, upgrade paths and runbooks included.

Hand Off

Your engineers pair with ours throughout, then own the platform. What we learned in your environment goes back into product.

The pod

Two roles. One accountable team.

Forward Deployed Engineer

A software engineer who builds in your environment. Writes the controllers, templates, pipelines and glue code between your infrastructure and AI stack, and the business need — and debugs them where they run.

Kubernetes, GPU and bare-metal automation
Model serving and inference pipelines
Integrations with your identity, network and data platforms
Agentic solutions
Migration off legacy virtualization and private cloud

Deployment Strategist

A technical lead who works with your stakeholders to define the outcome, map the path to production and clear the organizational blockers that stall infrastructure programs.

Outcome definition and success criteria
Security, compliance and procurement alignment
Enablement plan and handoff to your team

Field-to-product loop

Mirantis Forward Deployed Engineers have a direct line to Mirantis product engineering.

When your environment exposes a gap, it gets fixed upstream — not worked around in a fork only you maintain.

What we deploy

The work we take all the way to production.

AI factory bring-up

Bare-metal provisioning, GPU operators, high-speed fabric and multi-tenant clusters on NVIDIA and AMD hardware. 

Inference in production

Model serving, autoscaling, routing and observability for the models and agents your business depends on.

Agents in the real world

Move agents from demo to daily use without new risk. Every agent is secured, governed and auditable before it touches production.

Sovereign and regulated AI

Air-gapped and in-country deployments built to your security, residency and compliance requirements.

Migration and modernization

Moving workloads from legacy virtualization and private cloud onto a Kubernetes-native foundation, with no big-bang cutover.

Team enablement

Pairing, hands-on workshops and certification paths so your engineers own what was built. 

Engagements

Start small. Scale with the outcome.

TYPICALLY 2–4 WEEKS

Discovery sprint

A Deployment Strategist and an FDE assess your environment, agree the target outcome and deliver a working proof in your infrastructure.

You get: outcome definition, architecture, working proof, deployment plan

TYPICALLY 8–12 WEEKS

Deployment pod

An embedded pod takes one defined outcome to production, working alongside your engineers in your repos and environments.

You get: a production service, runbooks, trained owners

MOST COMMON

TYPICALLY 6–12 MONTHS, RENEWABLE

Embedded program

Multiple pods across sites or business units for large AI factory, sovereign cloud or modernization programs. 

You get: a program roadmap, delivered milestones, a self-sufficient platform team

LET'S TALK

Get in touch with Mirantis.

Whether you're planning a new platform, considering a migration, or just exploring options, we'll route your message to the right Mirantis team and get back to you within one business day.