The situation
You sell an AI product to enterprises. The sales cycle closed, the customer is keen, and the deployment has been “two weeks away” for two months. The blockers are not in your product. They are in the customer’s identity provider, the data connector their security team has not approved, the network rule that prevents your service reaching their document store, and the evaluation their business owner wants on their own data before anyone signs.
Meanwhile your product engineers are being pulled into every escalation, and the same integration problems recur on every account without reaching the product backlog. That is the work a forward-deployed engineer exists for: someone who sits on your side, works in the customer’s environment, and connects what happens there to what your product team builds next.
What the work involves
Forward-deployed work is half engineering and half making dependencies visible. For each customer I:
- Write the remit first. What is being deployed, into which environment, what “accepted” means, and who on each side has the authority to accept it.
- Map the dependencies. Identity integration, data sources and their owners, network access, model hosting and data-residency constraints, the customer’s security review. Each item gets a customer owner and a date.
- Integrate and configure. Connect your product to the customer’s systems within the access they grant, using your deployment tooling so the work is repeatable.
- Evaluate on their data. Generic benchmarks do not convince a business owner. Agree a small evaluation set from their real workflows and run it before the acceptance meeting.
- Feed back. When the same workaround is needed for a third customer, it becomes a product backlog item with evidence from all three.
The signature deliverable
Each deployment is anchored by a customer deployment remit, an integration dependency map and a clear statement of acceptance ownership. Illustrative example of a dependency map extract:
| Dependency | Customer owner | Your owner | Needed by | Status | Blocks |
|---|---|---|---|---|---|
| SSO via customer identity provider | Identity team lead | Me | Before pilot users | Waiting on app registration | User access |
| Read access to policy library | Knowledge manager | Me | Before evaluation | Approved, connector in test | Evaluation |
| Security questionnaire | Information security | Your security lead | Before production data | Returned with two questions | Production data |
| Acceptance test sign-off | Business owner | Account manager | End of pilot | Test cases agreed | Go-live |
Illustrative example showing the format, not a record from a client engagement.
How acceptance is judged
Two kinds of acceptance matter. The customer’s business owner accepts the deployment against the test agreed in the remit, run on their data. Your manager accepts my work as a member of your team: remits written, dependencies surfaced early, the product team receiving useful evidence, and the account team never surprised. Both are written down before work starts in each customer environment.
Ownership and handover
The customer relationship, commercial terms and product decisions remain yours throughout. I work under your standards and never commit your company to scope or dates. At the end of the contract, your solutions team receives the remits, the open dependency maps, the evaluation sets per customer, and a deployment playbook built from what recurred, so the next engineer starts from what was learned.
When to choose something else
If you want a partner to take on customer or product responsibilities across accounts, rather than an engineer in your team, use the software vendor partnership route. If one deployment should be delivered as a scoped outcome, commission forward-deployed AI engineering. If the bottleneck is in your own product rather than customer environments, the contract LLM engineer or AI platform engineer role is a better fit.