What does a Forward Deployed Engineer do?
A Forward Deployed Engineer, or FDE, works closely with a customer's team to understand a problem and implement a solution in its real operating environment. In AI projects, that can include discovery, integrations, evaluation, deployment and helping people adopt the system.
My job is to connect the business need to an implementation your team can operate.
When to bring me in
- The demo needs constant supervision. People still copy data, prompt each step and move results between tools.
- The problem is clear; the build is not. Requirements, exceptions and access decisions need working through with the team.
- Rollout needs an owner. Engineering needs help connecting the system, checking failures and preparing handover.
Start with an existing prototype or a new workflow. We scope one useful change before widening the engagement.
Who owns what
We agree the division of work before implementation. A typical scope can include:
| Stage | I handle | Your team provides |
|---|---|---|
| Discovery | Workflow map, constraints and proposed scope | Real examples and a workflow owner |
| Design and build | Agreed agent, integrations, checks and approval gates | Requirements, approved access and decisions |
| Rollout | Evaluation, deployment preparation and known limitations | Review of evidence and release approval |
| Handover | Run instructions and remaining work | An operating owner and support decisions |
The scope also names dependencies and work your existing engineers will own, so responsibilities remain clear.
How we work together
- Walk through a real run. Understand the manual work, exceptions and decisions. Agree the baseline and a useful pilot.
- Build with the users. Review actual outputs with your team and turn missed requirements into checks.
- Prepare the handover. Document operation, failure handling, verified behaviour and unresolved limitations before rollout.
Read my working method for the full process.
Public work you can inspect
- raza-Developer: a development workflow with plan approval, tests, evaluation and a delivery report.
- Raza SOR: my published record, working methods and the sources behind my claims.
- Engineering journey: a dated account of my work across predictive AI, MLOps and agent systems.
These show my own work. Client identities and private system details remain private.
Agree what success means
Choose measures before building: accepted results, errors, escalations, human operating time, running cost or adoption. Compare them with the starting point.
Counting automated steps helps map a process. It does not measure time saved or return on investment. Make a first pass with count your own workflow.
Questions about an FDE engagement
How is this different from commissioning an AI system?+
An FDE engagement includes working with your team to resolve the problem, constraints and rollout. A well-specified development project may need less discovery. Both can include implementation.
Can you work alongside our engineers?+
Yes. We agree responsibilities, reviews and deployment ownership. Your engineers keep their product context; I contribute the AI implementation and delivery work in our scope.
Do you work remotely or on site?+
I am based in Karachi, Pakistan (UTC+5). We agree time-zone overlap, working location and any on-site requirements when scoping the engagement.
Can you improve an existing system?+
Yes. We inspect what works, where it fails and what users need, then decide whether to extend it, simplify it or replace a focused part.
How is confidential information handled?+
We agree what data is needed, who can access it and where it is processed or logged. Each component receives only the access its job requires.
How are cost, duration and support agreed?+
They follow the workflow, integrations and delivery responsibilities. After the first conversation, we agree scope, terms and any ongoing support before implementation.