--- title: "Forward Deployed Engineering | Margins" description: "Put engineers where the outcome happens — senior engineers close to your operation, accountable for what works in production." url: https://margins.agency/services/forward-deployed-engineering --- Forward Deployed Engineering # Put engineers where the outcome happens. The hardest problems rarely reveal themselves in the specification. They appear when technology meets real users, real data, real systems and the realities of day-to-day operations. Margins Forward Deployed Engineers work close to your business — understanding what happens in production, solving what gets in the way, and continuously adapting the technology until it delivers the intended outcome. - Talk to an FDE - See how it works ↓ Engineering measured by what works in the business, not what was delivered against the specification. Reality starts after deployment ## The specification is an assumption. Production is reality. Before deployment, we can - Understand the process. - Interview users. - Analyze data. - Design workflows. - Define requirements. - Test the system. Once technology enters the real business - Users behave differently than expected. - Data contains edge cases. - Processes have exceptions nobody documented. - Integrations behave differently under real load. - The model encounters information it hasn’t seen before. - The workflow makes sense technically but creates friction operationally. - Sometimes the original assumption about the problem itself turns out to be incomplete. The difference between software that was delivered and software that creates value is what happens next. The FDE model ## Engineers close enough to the business to understand what needs to change. Traditional delivery moves information through layers. By the time the engineer sees the problem, they see a ticket. Forward Deployed Engineering shortens that distance. Traditional delivery 1. Business 2. Product Owner 3. Project Manager 4. Business Analyst 5. Engineering Information moves through layers. The engineer receives a ticket. Forward deployed - Users - Operations - Data - Systems - Management **FDE** ⇅ Production Engineering The engineer doesn’t just receive requirements. They understand why the requirement exists. The role ## Part engineer. Part problem solver. Close to the operation. A Forward Deployed Engineer is a senior technical professional who works directly with the customer and stays close to the environment where the technology is actually being used. Instead of waiting for perfectly defined requirements, they investigate problems, work with users, understand constraints, make technical decisions and implement solutions. 01 Business problem 02 Operational reality 03 Technical system Their job isn’t simply to write code. Their job is to make the technology work for the business. In the field ## From problem to production without the handoffs. 01 ### Work with users Observe how people actually use the system and understand where reality differs from the original design. 02 ### Investigate problems Go beyond tickets to understand the underlying operational or technical cause. 03 ### Build and adapt Implement changes across AI, software, data, integrations and workflows. 04 ### Handle edge cases Identify the exceptions that emerge only once a system meets real-world conditions. 05 ### Connect systems Solve integration problems across enterprise platforms, APIs, databases and internal software. 06 ### Improve AI performance Evaluate outputs, investigate failure modes and improve models, prompts, retrieval, tools and surrounding logic. 07 ### Measure outcomes Understand whether the system is actually producing the business result it was designed for. 08 ### Find what comes next As engineers learn the operation, they often uncover additional opportunities for automation and improvement. A different model ## Not staff augmentation. You’re putting engineering closer to the problem. Staff augmentation provides people against roles. Forward Deployed Engineering provides technical ownership around an outcome. Traditional staff augmentation Forward Deployed Engineering Assigned to a role Assigned to a problem or outcome Works from requirements Helps discover requirements Receives tickets Investigates what needs to change Primarily technical context Technical + operational context Delivery measured in output Success measured in what works Customer coordinates disciplines FDE works across the problem Often separated from users Works close to users and operations The goal isn’t more engineering hours. It’s less distance between the problem and the people capable of solving it. AI in production ## AI systems can’t be fully specified before they meet reality. Traditional software `if X happens → do Y` AI systems `thousands of variations in language, documents, images, behaviour and context` That makes the feedback loop after deployment significantly more important. The FDE observes 1. 01 Where is the AI wrong? 2. 02 Where is confidence too low? 3. 03 Which edge cases keep appearing? 4. 04 What information is missing? 5. 05 Where should a human intervene? 6. 06 Which users aren’t adopting the system? 7. 07 Where is the workflow creating friction? 8. 08 Is the system actually changing the business metric? Production becomes part of the development process. Continuous improvement ## Build. Observe. Learn. Improve. Instead of waiting weeks for operational feedback to travel through layers of management and backlog prioritization, the engineer sees what is happening and can act. Signals the FDE watches - AI performance - Business KPIs - Exceptions - System reliability - Operational feedback The FDE closes the feedback loop between what happens in production and what engineering does next. Where FDEs work ## Wherever technology meets a complex operation. 01 **AI implementations** Stay close to users and continuously improve AI systems after launch. 02 **Operational automation** Understand exceptions and extend automation as real-world complexity emerges. 03 **Enterprise integrations** Work across ERP, CRM, internal systems, APIs and data infrastructure. 04 **New digital products** Move quickly between customer feedback, product decisions and engineering. 05 **AI transformation** Embed technical capability inside business units as new AI-enabled workflows are introduced. 06 **Complex legacy environments** Navigate systems and processes that can’t be fully understood from documentation alone. 07 **High-change environments** Support initiatives where requirements evolve quickly as the business learns. How we work ## Close to your team. Backed by ours. An FDE shouldn’t feel like an isolated contractor placed inside the customer. They have the entire Margins engineering organization behind them. Embedded with your business. Backed by 40+ technology professionals. 1. 01 — **A dedicated technical counterpart** — Someone who develops deep context around the business and system. 2. 02 — **Direct access to the operation** — The FDE works with the people closest to the problem rather than only through tickets. 3. 03 — **Margins engineering depth** — When the problem needs deeper expertise in ML, data, cloud, frontend, backend, computer vision or architecture, the FDE brings the wider Margins team in. 4. 04 — **Continuity** — Knowledge accumulates instead of being repeatedly transferred between disconnected teams. From ticket to outcome ## Shorter distance. Faster learning. Better decisions. Same problem, two routes to a fix. Traditional delivery 10 steps 1. 01 User finds problem 2. 02 Reports it 3. 03 PM interprets it 4. 04 Ticket created 5. 05 Engineering receives it 6. 06 Questions return 7. 07 Ticket refined 8. 08 Solution built 9. 09 User tests it 10. 10 New problem discovered …and the loop starts again. Forward deployed 8 steps 1. 01 FDE sees problem with user 2. 02 Understands context 3. 03 Investigates system / data 4. 04 Defines solution 5. 05 Builds with engineering 6. 06 Deploys 7. 07 Observes result 8. 08 Improves Learning stays with the engineer who acts on it. After deployment ## Go-live is where Forward Deployed Engineering begins to matter most. The software is live. Now real users interact with it. Real data flows through it. Real exceptions appear. And the assumptions made during design finally meet reality. 1. 01 **Deploy** 2. 02 **Train** 3. 03 **Adopt** 4. 04 **Forward Deployed Engineering** *This service* 5. 05 **Operate** 6. 06 **Improve** Two sides of production AI ## Systems need both operations and evolution. ### Change the system. - Works close to users and operations. - Investigates problems. - Implements improvements. - Adapts workflows. - Solves new requirements. - Improves how technology fits the business. Managed AI ### Keep the system running. - Monitors infrastructure. - Tracks model / system performance. - Manages reliability. - Observes usage and cost. - Maintains production operations. - Responds to operational issues. [Explore Managed AI →](https://margins.agency/services/managed-ai) Managed AI keeps the system healthy. Forward Deployed Engineering keeps it moving forward. How we measure success ## Shipping isn’t the outcome. Engineering output matters. But the ultimate question is whether the system is changing what it was built to change. That might mean: - Less manual work - Faster operations - Higher employee adoption - Better decisions - Fewer errors - Earlier detection - Lower operating cost - More revenue What the system exists to change The FDE is the engineering function closest to that outcome. Getting started ## Put the right engineer close to the right problem. Every engagement starts with a mission — the problem, outcome or production responsibility the FDE will own. Not a job description. 1. 01 — Understand the environment — We learn the business objective, existing system, technology environment and operational context. 2. 02 — Define the mission — Agree on the problem, outcome or production responsibility the FDE will own. 3. 03 — Match the engineer — Select an engineer with the technical depth and working style appropriate to the environment. 4. 04 — Embed — The FDE works directly with your operational and technical teams. 5. 05 — Build the feedback loop — Establish direct access to users, production signals, business KPIs and the broader Margins engineering team. 6. 06 — Improve continuously — Prioritize and execute against what is actually happening in the business. When FDE is the right model ## Forward Deployed Engineering works particularly well when… If the work can be completely defined, handed to engineering and delivered against a stable specification, **you probably don’t need an FDE.** - The technology is already in production but needs continuous adaptation. - The problem can’t be completely specified upfront. - Users and engineers need a much tighter feedback loop. - AI behaviour needs to be evaluated against real-world conditions. - The implementation spans multiple systems and business processes. - Operational complexity creates frequent exceptions. - The business is moving too quickly for traditional requirement handoffs. - You need senior technical ownership without building an entire specialist AI team internally. Why Margins ## Forward deployed, with an engineering company behind them. 01 ### AI-native engineering FDEs work across modern AI systems, software, data and enterprise integrations. 02 ### Business context The role is designed around understanding the operation, not just the codebase. 03 ### End-to-end capability An FDE can pull in AI, backend, frontend, data, DevOps, UX and architecture expertise when needed. 04 ### Production mindset We optimize for what works after deployment, not merely what passes acceptance. 05 ### Outcome orientation The engineering loop remains connected to the reason the system was built. 06 ### Proven delivery organization One accountable team, with continuity between the people who scope and those who build. **40+** Skilled professionals **60+** Projects delivered **$100M+** Measured client impact **#11** Deloitte Technology Fast 50 Central Europe ## Related capabilities ### [AI Engineering](https://margins.agency/services/ai-engineering) Build production AI systems around your data, workflows and existing technology. Explore AI Engineering → ### [Managed AI](https://margins.agency/services/managed-ai) Operate, monitor and maintain AI once it enters production. Explore Managed AI → ### [AI Transformation](https://margins.agency/services/ai-transformation) Embed AI systematically across the organization. Explore AI Transformation → ### [AI Training & Enablement](https://margins.agency/services/ai-training) Prepare your people to work effectively with the technology being introduced. Explore AI Training → Bring engineering closer ## Put an engineer where the problem actually happens. If you have a complex AI or software system where success depends on understanding what happens after deployment, we’ll help put the right technical capability close to the operation. [Talk to an FDE](https://margins.agency/contact) Talk to our team → Close to the business. Close to the technology. Accountable for what happens between them.