Forward Deployed Engineering

Embedded with your team. Accountable from use case to outcome.

Rackspace Forward Deployed Engineers work inside your team to turn business challenges into working AI solutions, take them into production and keep improving them, with one team accountable from the first use case to measurable results.

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Create continuity from AI development through production

Nearly every technology leader plans to increase AI investment, yet only around a third have taken AI into production. Traditional delivery separates infrastructure, platform and application teams, creating handoffs between the integrator, the platform, the pilot team and the people responsible for production. Keeping those teams closely connected can help preserve context, accelerate decisions and carry ownership from the first use case into live operation.

driving your ai outcomes

Production in weeks. Ownership that lasts.

Speed to production

Parallel workstreams move use cases from prototype to production in weeks.

Embedded from day one

Engineers align to your culture, constraints and data, with less ramp-up and fewer handoffs.

Outcome ownership

Accountability for business results and system performance continues past go-live.

Value that compounds

Patterns from each engagement are reused, so every new system starts from what’s already proven.

0K+

certified technical experts

0K+

technical certifications

Palantir certifications

Uniphore certifications

How it works

From first conversation to working solution.

(1-3 days)

Discovery

Define the challenge, objectives, success metrics and technical landscape.

(2-3 days)

Team design

Build the engineering team around the use case, data and platforms involved.

(3-5 days)

Embed

Get engineers into your environment, systems and ways of working.

(2-12 weeks)

Build in sprints

Deliver iteratively against agreed KPIs, with a working prototype in the first weeks and continuous feedback.

Ongoing

Production and operation

Deploy onto governed infrastructure, share documentation and knowledge with your team and continue operating the system as requirements evolve.

Enterprise ai cloud

One team to connect silicon to outcomes.

FDEs are trained on the enterprise AI platforms in the Rackspace stack and on the governed private cloud beneath them, so one team decides how agents, models, data pipelines and infrastructure work together, with efficiency, cost and control in view. A typical team includes three to five engineers: core engineers and a data and AI specialist, with architecture and DevOps support as needed.

Technology changes. Customer choice remains. Rackspace stays accountable.

How we deliver

Build. Operate. Innovate.

The same engineers who build your AI solution run it in production and keep it improving. One team does all three.

Build

Built inside your environment

FDEs work within your tools, data and workflows, solving the specific problem in front of you, with decisions made close to the work.

Operate

Accountable in production

The engineers who build it run it on governed Rackspace infrastructure, alongside your critical applications, as requirements change.

Innovate

Faster with every deployment

Reusable pipelines, workflows and patterns from each engagement are refined and applied, so every new use case starts stronger.

Industries

Trusted expertise for industries where the stakes are the highest

Healthcare

Epic and clinical platforms kept available, patient data protected, and a governed foundation ready to move AI from pilot to the point of care.

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Financial services

Core banking and payments modernized with proven recovery, data control and the audit evidence regulators expect, so AI can follow safely.

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Public sector

Sovereign, accredited cloud for citizen services, run by cleared teams, with the control to modernize today and adopt AI when you’re ready.

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Explore Industries

“Our Rackspace team brought excellent AI experience. And they worked well with our internal engineers to execute the project and help us achieve our goals.”

Sudipto Das

VP of Engineering, Convera

Go to the Case Study

FAQ

Common questions about Rackspace Forward Deployed Engineering

What is a Rackspace Forward Deployed Engineer?

A Rackspace Forward Deployed Engineer (FDE) works directly with a customer's team to identify AI use cases, build and deploy AI solutions on governed infrastructure, and manage them in production. FDEs work with enterprise AI platforms, including Palantir, Uniphore and Rackspace's own technologies, maintaining operational accountability beyond go-live.

How is Rackspace Forward Deployed Engineering different from traditional AI consulting or systems integration?

Traditional AI consulting and systems integration often focus on designing and deploying solutions before handing them over to internal teams. Rackspace Forward Deployed Engineers take a different approach by remaining embedded with customers to operate and optimize the AI systems they build. With Rackspace also managing the underlying infrastructure, customers benefit from a single accountable team responsible for production operations and business outcomes.

How quickly can Rackspace Forward Deployed Engineers deploy AI solutions into production?

Rackspace Forward Deployed Engineers can typically deliver working AI prototypes within one to two weeks. By running development and deployment activities in parallel, they help enterprises move AI solutions from prototype to production in weeks rather than months, potentially accelerating deployment by up to five times compared with traditional phased delivery models.

How do Forward Deployed Engineers relate to Rackspace Elastic Engineering?

Both embed Rackspace engineers alongside the customer’s team. Forward Deployed Engineers deliver AI workloads into production on Enterprise AI Cloud. Elastic Engineering pods deliver cloud engineering work such as migration, modernization and automation in weekly sprints.

Why choose Rackspace Forward Deployed Engineers instead of building an internal AI team?

Rackspace Forward Deployed Engineers complement internal AI teams by providing specialized expertise in AI development, deployment, MLOps and governance. Rather than spending months building these capabilities internally, organizations can work with embedded Rackspace engineers to accelerate AI deployment while transferring knowledge, workflows and best practices to their teams.

How does Rackspace Forward Deployed Engineering reduce AI deployment risk?

Rackspace Forward Deployed Engineering helps reduce AI deployment risk through an incremental, use-case-driven approach. Each project starts with defined KPIs, progresses through iterative development sprints and is evaluated against measurable outcomes before expanding. AI systems run on governed infrastructure with security and compliance controls designed to support regulated environments.

Where does Rackspace Forward Deployed Engineering deploy and operate AI workloads?

Rackspace Forward Deployed Engineers deploy and operate AI workloads on Rackspace-managed infrastructure, including Governed AI Private Cloud for regulated and data-sensitive environments, public cloud platforms and edge locations such as hospitals and sovereign facilities. This flexible approach supports different business, security and compliance requirements while maintaining a single accountable operator.

Who owns the intellectual property developed through Rackspace Forward Deployed Engineering?

Customers retain ownership of their data and customer-specific intellectual property developed during Forward Deployed Engineering engagements. Rackspace may reuse general technical patterns, workflow structures, pipeline designs and infrastructure templates to accelerate future deployments, while maintaining customer-specific IP ownership and data protection.

Bring us the use case. We’ll bring the team.

Talk to a Rackspace Forward Deployed Engineering lead about the AI initiative that needs to be in production this quarter.