Forward Deployed Engineers
What is a forward deployed engineer?
A forward deployed engineer (FDE) works inside your organizational context to build and integrate AI solutions using first-hand knowledge of your systems and goals. These client-facing, outcome-aligned engineers help determine what should be built, take ownership from design to deployment and monitoring, optimize in production, and drive the data, architecture, and integration work that the AI solutions depend on.
As an AI-native engineering partner, 10Pearls deploys senior engineers with the expertise to operationalize AI across complex enterprise environments. They bring experience spanning data, applications, cloud, integration, and governance, allowing them to work through the technical and operational challenges.
Where forward deployed engineers add value
AI piIots
stall before production
AI doesn’t
fit current workflows
Unclear or
evolving requirements
Enterprise data isn’t
AI-ready
Improve the data ecosystem for AI adoption based on real usage and client goals. Resolve data quality issues and set agent permissions that improve retrieval and grounding quality.
Legacy systems block
AI adoption
Focus modernization on your actual AI expectations and relevant legacy constraints discovered through implementations, to accelerate adoption and reduce unnecessary cost and effort.
Agents require
continuous evaluation
Optimize AI agent behavior in production, using evaluation and guardrails to identify the root cause and make appropriate adjustments across models, tools, context,
and permissions.
Capabilities of our AI forward deployed engineers
AI solution & agent engineering
Rapidly deliver production-grade AI solutions and orchestrate reliable agents against actual operational needs through direct contact with the users and workflows.
Data & knowledge integration
Improve AI accuracy and grounding by connecting solutions to trusted enterprise data and knowledge, resolving access, quality, and retrieval gaps through real use.
Enterprise application integration
Increase workflow efficiency by integrating AI into the applications users already leverage, uncovering dependencies and exceptions documentation rarely never records.
AI evaluation,
quality & guardrails
Improve AI reliability, quality, and safety through continuous evaluation of AI behavior in production. Strengthen guardrails and controls as new risks emerge.
Cloud & platform engineering
Enhance AI scalability, resilience, and improve cost control by shaping cloud and platform foundations around workloads, traffic, and constraints measured in production.
End-to-end
operational enablement
Streamline adoption and enhance operational value by aligning AI with the roles and decisions it supports. Help build internal technical capability to run and grow AI systems.
How our forward deployed AI engineers work
Align & embed
We identify the right forward deployed software engineers based on your AI needs, industry, and operational requirements. Once the fit is confirmed, they are embedded in your environment and aligned to the relevant workflows and priorities.
Understand & plan
Our FDEs learn about your operations and AI goals, then discover constraints, opportunities, and viable paths to AI adoption by directly interacting with your systems and data. They plan the solution, integrations, and the architectural changes it depends on.
Prototype & validate
Early prototypes are built, tested, and validated in a sandbox environment and then against governed data and real parameters in test deployments, with rollback mechanisms built in. The decision to proceed or change approach rests on evidence.
Build & integrate
Client-approved prototypes are built for your production environment, scalability requirements, and constraints. Our FDEs leverage deep legacy systems experience to configure and, if needed, build AI integrations that allow the solution to deliver and scale in production.
Operationalize & govern
Governance and AI security controls are built into the solution to your regulatory and internal policy requirements, alongside deployment pipelines and monitoring. These controls and guardrails are reconfigured and expanded as the behavior of the AI system is observed in operation.
Forward deployed engineers compared with other delivery options
| Delivery option | Best suited to | How priorities are set | Primary accountability | Typical limitation |
|---|---|---|---|---|
| Forward deployed engineer | Ambiguous, high-priority problems that must be solved in the operational environment | Priorities evolve through discovery, implementation and live feedback | Operational usefulness, production performance and adoption | Less suitable for routine or fully specified work |
| Embedded software engineer | Adding engineering capacity to an existing team and roadmap | Primarily through the client's backlog and engineering leadership | Quality and completion of assigned engineering work | Problem discovery or broader operational outcomes |
| AI consultant | Strategy, opportunity and AI assessment, governance design and transformation planning | Advisory scope and agreed deliverables | Quality of recommendations, decisions & roadmap | May not remain to build and operate the solution |
| AI development team | Building a defined AI application, platform or product | Project scope, requirements and delivery plan | Solution delivery against agreed requirements & outcomes | Requires sufficient clarity around the intended solution |
| Solutions architect | Designing an implementation architecture or platform approach | Technical requirements, reference patterns and platform constraints | Architectural quality, scalability and technical fit | May design and guide without owning end-to-end implementation |
| Platform implementation specialist | Configuring and deploying a particular vendor platform | Platform functionality and implementation requirements | Successful configuration, integration and launch | The platform can become the starting point rather than the client problem |
Our enterprise AI engagements
case study - HEALTHCARE
Building a Clinical-Grade AI Platform for GLP-1 Weight Care
Designing and building a HIPAA-aligned platform that pairs a patient app and clinician portal with a dual-pipeline clinical AI.
Case study -Llegal Services
Accelerating Critical Business Workflows with AI Automation
Improving speed, consistency, and risk control in a high-volume legal workflow through AI-powered automation.
Case study - Artificial Intelligence
Agentic AI Financial Assistant
Optimizing financial operations with an AI-powered smart assistant designed to automate workflow and enhance decision-making.
Why enterprises hire forward deployed engineers with 10Pearls
AI-native engineering
Apply AI as part of the wider system, connecting models and agents with the data, applications, workflows & controls required to create operational value.
Operational judgment
Draw on experience across enterprise systems, legacy environments and regulated operations to identify constraints, make sound trade-offs and choose viable paths.
Production-grade engineering
Build and refine AI against real users, data, traffic and dependencies so performance, reliability and cost hold beyond pilot conditions.
Built for regulated environments
Embed security, governance, and human oversight into solution architecture, supporting reliable adoption within regulatory and policy requirements.
Platform-flexible delivery
Work across the AI, cloud, data and application platforms already in place, shaping the solution around the operational problem rather than a single vendor roadmap.
Time-zone aligned collaboration
We leverage our global footprint to maintain meaningful overlap with your working hours and stay close to users, decisions and emerging issues.
Engagement options
One forward deployed engineer
Best for: A single high-priority AI problem where the path to production is not yet clear.
What you get: One senior FDE embedded in your environment, working across business, data, and technology stakeholders.
- Clarity on the production path and its constraints
- A working solution validated in your environment
- Evidence to guide the decision to scale
An FDE pod
Best for: Complex problems spanning AI, data, integration, and platform work.
What you get: Several engineers covering the disciplines the problem needs, operating as one embedded unit.
- Delivery without handoffs between specialist teams
- Faster resolution of cross-system dependencies
- Production readiness across every layer involved
A standing team
Best for: Enterprises focusing on multiple AI priorities that continue to evolve across functions and business units.
What you get: A persistent team that retains context across priorities rather than restarting with each initiative.
- Context and methods that carry across initiatives
- Consistent governance across your AI portfolio
- Internal AI capabilities built side by side
Insights
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FAQs about forward deployed engineers
What is a forward deployed engineer?
A forward deployed engineer refers to a senior engineer working with client operations and benefiting from direct access to organizational context. They leverage this access to identify opportunities and viable AI use cases, plan and execute builds, handle integration and operationalization, while improving solutions in production as they interact with users, operational data, and constraints.
What is an AI forward deployed engineer?
An AI forward deployed engineer, sometimes called an applied AI engineer, helps organizations operationalize AI by working within their operational context, interacting directly with data and users, discovering constraints, and identifying viable paths to delivery. This proximity matters because AI behavior cannot be fully specified before it meets real data.
How is a forward deployed engineer different from a consultant?
An engineering consultant offers recommendations and may design and build solutions based on a customer’s requirements, the information customers convey, and their own assessments. A forward deployed engineer builds, integrates, and improves solutions from within the client’s operations, with direct proximity to context ensuring nothing gets lost in translation.
When should an enterprise hire a forward deployed engineer?
It’s smart to hire a forward deployed engineer when organizations understand the problem but haven’t figured out a viable path to production. In these situations, success depends on adoption inside existing workflows to handle evolving requirements as the system meets real data and users. However, when you have clarity on how the build should go or know how much engineering capacity you need for specific problems, other models might be a better fit.
Should we hire one FDE or a team?
The answer depends upon the problem you are trying to solve. But in many cases, an engagement might start with a single engineer working against a specific priority, and organizations may expand to a pod or a standing team as the problem evolves.
What happens when an FDE engagement ends?
FDEs focus on AI enablement throughout their deployment within client organizations, helping them become familiar with the solution as it is built and changes made to accommodate that. When an engagement ends, a formal handover covers documented decisions and pairing with the people who will use the solution.