Hire an AI Forward Deployed Engineer
AX3 AI forward deployed engineers embed in your environment to take AI use cases past the demo: grounding and retrieval on your real data, evaluation harnesses, permissions, cost and latency control, deployment and monitoring. Engagements typically start within two to three weeks and are scoped around a production AI outcome rather than a research exercise.
Talk to usWhat an AI forward deployed engineer actually does
Most enterprise AI failures are not model failures. They are grounding, permission, evaluation and operations failures. An AI FDE works on exactly those: connecting the model to trustworthy data, making sure users only see what they are entitled to see, measuring quality with an evaluation set that reflects the business, and running the thing reliably at a cost the finance team accepts.
AI FDEs work across Lovable-style rapid iteration and enterprise platforms alike — open-source and hyperscaler models, retrieval pipelines, agent frameworks, and Salesforce Agentforce and Data 360 where the workflow lives in CRM.
Grounding & retrieval
Document and data pipelines, chunking, embeddings, hybrid search and permission-aware retrieval on enterprise content.
Evaluation & guardrails
Evaluation harnesses, regression sets, output validation, PII handling and human-in-the-loop review paths.
Agents & workflow
Tool-calling agents wired into real systems — CRM, ERP, ticketing and internal APIs — with safe action boundaries.
AI operations
Latency and cost engineering, observability, prompt and model versioning, and rollout controls.
Four ways to engage — with pricing model and speed to start.
| Model | Best for | Pricing model | Speed to start |
|---|---|---|---|
| Single AI FDE | Taking one high-value AI use case to production | Monthly engineer rate | 2–3 weeks to start |
| AI FDE + Pod | Multiple use cases and a shared AI platform layer | Fixed monthly pod cost | 3–4 weeks to a running team |
| Outcome-scoped | A defined production AI capability with acceptance criteria | Milestone-based commercial model | Scoped in 1–2 weeks |
| Contract-to-Hire | Building an internal AI engineering team | Engagement rate, then an agreed conversion fee | 2–4 weeks |
Swipe the table sideways to see all columns.
Placed where AX3 already delivers.
Semiconductor & Hi-Tech
Engineering copilots, yield analysis assistants and knowledge retrieval.
View skillsFinancial Services
Advisor assistants, document processing and compliance-safe summarisation.
View skillsHealthcare & Life Sciences
Clinical and regulatory document intelligence with strict data boundaries.
View skillsCPG & Retail
Demand signals, trade promotion insight and service assistants.
View skillsHire an AI Forward Deployed Engineer — frequently asked questions
What does an AI forward deployed engineer do that an AI/ML engineer does not?
An AI/ML engineer builds models and pipelines. An AI FDE is embedded with the business owner and is accountable for the use case running in production — grounding, permissions, evaluation, cost, adoption and handover included.
Do you work with our own model provider?
Yes. Engineers work with your chosen models and hosting — hyperscaler, open-source or platform-native such as Salesforce Agentforce — and will advise where the choice materially affects cost, latency or data boundaries.
How do you prove an AI feature is good enough to ship?
With an evaluation harness built from your own examples, plus regression runs on every change. Ship criteria are agreed before build, not argued after the demo.
How do you keep AI costs predictable?
Through retrieval design, caching, model routing and observability on token spend per workflow, reviewed as an explicit engineering constraint.
Talk to AX3 about AI forward deployed engineering
Tell us which AI use case is stuck at prototype. We will embed engineers who have shipped that pattern into production.