Forward Deployed Engineering
Engineers embedded in your environment, accountable from use case to outcome.
Rackspace Forward Deployed Engineers (FDEs) work inside your team to define, build, deploy and operate AI in production on governed infrastructure, accelerating time to production by up to 5x with continued support beyond go-live.
Create continuity from AI development through production
Operating model
Embed. Compound. Operate.
Embed
Inside your environment from day one
FDEs work within your tools, data and workflows alongside your team, solving the specific problem in front of you. Decisions are made close to the work, helping teams move faster as the use case takes shape.
Compound
Every deployment strengthens the next
Teams produce reusable ontologies, pipelines, workflows and infrastructure patterns, then refine and apply them to future systems. What you build in one environment gives the next deployment a stronger starting point.
Operate
Accountable in production
FDEs run the systems they build alongside your critical applications on governed Rackspace infrastructure and remain engaged as requirements evolve, helping maintain system performance and operational continuity.
What changes
Production in weeks, with ownership that lasts.
Speed to production
Run workstreams in parallel, moving from prototype to production in weeks.
Embedded from day one
Engineers work alongside your team, aligned to your culture, constraints and data, with fewer ramp-up and handoff delays.
Outcome ownership
Keep accountability for measurable business results and system performance beyond go-live.
Value that compunds
Reuse patterns from each engagement so each system benefits from what has already been built and proven.
How it works
From first conversation to production in weeks.
Discovery (1–3 days):
Team design (2–3 days):
Embed (3–5 days):
Build in sprints (2–12 weeks):
Production and operation (ongoing):
Full-stack engineering
Connect data, AI and infrastructure in one team
FDEs are trained on the enterprise AI platforms in the Rackspace stack and on the governed private cloud beneath them. One team can then determine how agents, models, data pipelines and the underlying infrastructure work together, with efficiency, cost and control in view. A typical engagement team includes three to five engineers: core engineers, a data and AI domain specialist, plus architecture and DevOps support as needed, all working on AI-assisted tooling. Data, applied AI and applications progress together as one team builds and operates across the stack.
Explore Enterprise AI Cloud
Industrias
Pericia de confianza para industrias donde hay más en juego
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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“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
Enterprise AI
Connect your FDE team to the full Enterprise AI Cloud
Forward Deployed Engineers work across the infrastructure, AI platforms and advisory capabilities that support your use case from planning through production and ongoing operation.
Enterprise AI Cloud
Connect your use case to the full stack, from governed private cloud to AI inference and agents in production.
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Governed AI Private Cloud
Build data-sensitive and regulated AI workloads on the governed infrastructure FDEs use to take them into production.
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Advisory Services
Prioritize use cases and establish AI governance with guidance that carries through to production.
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CONTACT US
Bring us the use case. We’ll bring the team.
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.