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hire an FDE
Hire forward deployed engineers
Hire forward deployed engineers when an AI workflow needs somebody to own discovery, integration, testing and rollout inside your business. predeploy provides embedded engineering engagements; permanent recruitment is a different route with a different commitment.
Leave the scoping call with a clear first workflow, delivery responsibilities and the right hiring route.
OpenAI describes FDE ownership from discovery through production rollout. OpenAI: forward deployed engineer role ↗
Anthropic describes engineering embedded with customer teams. Anthropic: staff forward deployed engineer role ↗
New to the topic? Read the background on artificial intelligence.
hire an FDE
what should you establish before hiring?
Name the workflow, its owner and the result the engineer must deliver.
Bring a real example of the work, the tools involved and the people whose approval is needed. Explain the current process, including the awkward cases. A brief that only says “we need an AI agent” leaves the important decisions to discovery after the person starts.
If those inputs are missing, begin with AI assessment. If you mainly need the role explained, read forward deployed engineering. The right brief makes clear which decisions the engineer can take and which remain with your team.
A named workflow and business owner
A sample of inputs and accepted outputs
Access and approval requirements
hire an FDE
should the role be permanent or embedded?
Choose permanent headcount for sustained work and an engagement for a bounded delivery need.
A permanent engineer can accumulate product knowledge and support successive releases. Your company must provide a roadmap, management and enough work to justify that ongoing commitment. An embedded engagement instead starts with a defined piece of delivery and an agreed handover.
Our engineers for hire page explains how to scope that engagement. For a permanent search, use the assessment stages in how to hire forward deployed engineers and distinguish a recruitment fee from engineering delivery fees.
hire an FDE
what should an engineering brief contain?
Give the engineer the boundaries of the problem and the evidence that will count as success.
List the systems that must be integrated and who can grant access. State the information that may be used, any actions requiring human approval and the existing fallback. Write down what is excluded so that the first build cannot quietly become a programme of unrelated requests.
A useful illustrative brief asks for a reviewed sales research pack from approved sources, rather than autonomous outbound messages. AI-powered GTM explains that revenue workflow; AI planning consultancy can turn the opportunity into a delivery sequence.
| Check | What to establish |
|---|---|
| 1 | Inputs, outputs and users |
| 2 | Acceptance examples and exclusions |
| 3 | Permissions, review and fallback |
hire an FDE
what will your team need to contribute?
An engineer needs decisions and access from the business as well as technical time.
A subject expert must explain what good work looks like and review the first outputs. A system owner must approve integrations. Someone with authority must resolve conflicts between speed, scope and risk. Without those counterparts an external engineer can produce code while the deployment remains blocked.
Keep a short decision log and make user testing part of the work plan. Pair the release with AI training for teams, so adoption is checked against a real task rather than attendance at a launch session.
hire an FDE
how are costs and handover agreed?
Scope and quote the actual engagement, including the work needed after the first demonstration.
Ask what the fee covers, how changes are priced and which costs stay with you. Hosting, model usage and third-party licences may be separate from engineering time. Agree the support arrangement and what happens when an external service or model changes.
The handover should include repositories, operating notes, test cases and a named internal owner. AI technical consultancy can review those requirements. Contact predeploy with the workflow and systems involved so we can prepare a plan and quote.
Separate delivery and operating costs
Agree change control and support
Transfer documentation and evaluation cases
Make the decision concrete
What does the current task cost in time?
Choose a task connected to hire forward deployed engineers and enter its current workload. This gives you a baseline to discuss before buying training or commissioning a build.
A baseline is useful even if you decide to keep the process manual. Measure the work that matters, then compare quality and review effort as well as time.
Assess the workflow before changing it, or plan business AI training if the gap is how people use existing tools.
Your current workload
Estimate how much time the existing task takes before discussing hire forward deployed engineers.
16 hours per week
People × minutes × weekly tasks ÷ 60. A workload baseline from your inputs, not a prediction of AI time savings. Include review and support time when comparing a proposed workflow.
A useful AI investment leaves your team able to do the work and judge the result.
Continue with the right question
Where should you go next?
Use the overview for context, then follow the pages that match your delivery decision.
See our forward deployed engineers service or read the forward deployed engineering overview.
FAQ
Questions, answered
Evidence and further reading
Which sources support the guidance?
Source links support the specific background described here. The practical checklists and illustrative examples are predeploy’s guidance.
OpenAI describes FDE ownership from discovery through production rollout. OpenAI: forward deployed engineer role ↗
Anthropic describes engineering embedded with customer teams. Anthropic: staff forward deployed engineer role ↗
NCSC guidance covers design, development, deployment and operation. NCSC: secure AI system development ↗
Background reading on the broader field of artificial intelligence. Wikipedia: artificial intelligence ↗
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Forward deployed engineering
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