Hire Forward Deployed Engineers (FDE) — On-Demand AI Deployment
Senior AI engineers embedded inside your environment to ship production AI — LLM, RAG and agentic systems that reach production in weeks, without winning a full-time talent war.
Ship Production AI Without the Talent War
Six capabilities every Forward Deployed Engineering engagement delivers — embedded in your environment, accountable to your business outcome.
Embedded Discovery & Scoping
A senior FDE works alongside your team to map the business problem, data landscape, integration constraints and compliance requirements. Output: a scoped engagement plan in 2 weeks.
Architecture & Integration Design
Production AI architecture spanning model selection, RAG and agentic workflows, integrated with your stack (SAML/OIDC auth, legacy databases, cloud, observability) — with stakeholder sign-off before any production code is written.
Production Code Delivery
Integration code, evaluation harnesses, agent workflows, RAG implementations and custom prompts, with weekly demos. Staging in 2–4 weeks, production in 8–12 weeks for typical engagements.
Evaluation Engineering
Automated test suites that catch hallucinations, drift and silent regressions before they hit production users — the non-negotiable that separates deployed AI from demo-grade AI.
Stabilization & Drift Management
Post-launch operation through model-provider updates, edge-case failures and prompt regressions. The FDE stays until the deployment is hitting its success metric reliably — not just at code merge.
Knowledge Transfer & Handoff
Documented runbooks, architecture decisions, evaluation infrastructure and 2–4 weeks of side-by-side knowledge transfer with your internal team, with an optional retainer for ongoing support.
Ready to ship production AI without the hire?
Book a free 30-minute strategy session. We'll map the fastest path to embedding a senior Forward Deployed Engineer in your environment — scoped to your deployment, your stack and your success metric.
Why Hiring an AI Engineer In-House Stalls
An in-house FDE in 2026 costs $300K–$500K+ in total compensation, plus $50K–$100K in recruiting overhead and 60–120 days of ramp — and that's if you win the hire at all. Postings for the role grew 800% between January and September 2025, so final-round candidates decline and the seat stays open for months while your deployment waits — and a competitor ships first.
Embed a senior FDE instead of hiring one.
A Forward Deployed Engineer embeds directly inside your environment and owns the deployment end to end — scoping, integration, evaluation and stabilization — so you ship production AI in weeks without the recruiting cost, the ramp or the bench tax.
How We Deliver a Forward Deployed Engagement
From embedded discovery to a stabilized, documented deployment your team can operate — accountable to your success metric, not just a code merge.
Embed & Scope
A senior FDE embeds within 7–14 days and works alongside your team to map the business problem, data landscape, integration constraints and compliance requirements. Output: a scoped engagement plan in 2 weeks.
Architect & Integrate
We design the production AI architecture — model selection, RAG, agentic workflows — and integrate with your stack: SAML/OIDC auth, legacy databases, cloud and observability. Stakeholder sign-off before any production code is written.
Ship to Production
Integration code, evaluation harnesses, agent workflows, RAG implementations and custom prompts, with weekly demos. Staging in 2–4 weeks, production in 8–12 weeks for typical engagements.
Stabilize & Hand Off
The FDE stays through model-provider updates, edge cases and prompt regressions until the deployment reliably hits its success metric, then delivers runbooks, architecture decisions and 2–4 weeks of side-by-side knowledge transfer.
Why Forward Deployed Engineering Beats Staffing
A forward deployed engineer is a senior AI/ML engineer who embeds directly inside your environment to design, build and ship production AI — instead of handing off code from a distance. Unlike a traditional consultant or staffing contractor, an FDE owns the deployment end to end: scoping the business problem, integrating with your existing stack, building evaluation harnesses and guardrails, and stabilizing the system in production until it reliably hits its success metric.
The model pairs vendor-grade engineering depth with on-site accountability, so teams ship production AI in weeks — without winning a full-time talent war. And it flexes to how you actually buy: project-based, retainer, fractional, or compliance-scoped for regulated deployments.
NeuralChainAI embeds a senior Forward Deployed Engineer in your environment, owns the deployment end to end, and hands your team a documented, stabilized system — LLM, RAG or agentic — that reliably hits its success metric.
What Our Forward Deployed Engineers Build
Production AI Agents
Agentic workflows built, integrated and stabilized inside your environment — shipped to production, not left at demo-grade.
RAG Systems
Retrieval-augmented generation implementations wired into your data and stack, with the guardrails that keep answers grounded.
Evaluation Harnesses & Guardrails
Automated test suites that catch hallucinations, drift and silent regressions before they reach production users.
Stack Integration
Integration code that connects production AI to your auth (SAML/OIDC), legacy databases, cloud and observability.
Frequently Asked Questions
Most engagements move from initial contact to embedded engineering work within 7–14 days. Discovery begins within the first week, with production code shipping in the first 4–8 weeks depending on scope — compared against 4–9 months of time-to-fill for an in-house hire.
An in-house FDE in 2026 costs $300K–$500K+ in total compensation, plus $50K–$100K in recruiting overhead and 60–120 days of ramp time. Our engagement-based pricing isolates the productive engineering hours — no recruiting cost, no ramp, no bench tax — and typical engagements deliver the same outcome at 40–60% lower total cost.
Yes. Fractional engagements allocate a senior FDE to your account 2–3 days per week, typically for 6+ months. It's best for companies that need senior deployment capability but don't have the work volume to justify full-time.
Every engagement concludes with a structured transfer: documentation, runbooks, evaluation suites, and 2–4 weeks of side-by-side knowledge transfer with your internal team. Optional retainer support is available for ongoing stabilization, drift management and incremental enhancement.
A forward deployed engineer (FDE) is a senior AI/ML engineer who embeds directly inside your environment to design, build and ship production AI, instead of handing off code from a distance. The FDE owns the deployment end to end — scoping the business problem, integrating with your existing stack, building evaluation harnesses and guardrails, and stabilizing the system in production until it reliably hits its success metric.
A traditional consultant advises and a staffing contractor fills a seat; an FDE owns the outcome. The model pairs vendor-grade engineering depth with on-site accountability — the engineer integrates with your auth, databases, cloud and observability, ships production code, and stays until the deployment is hitting its success metric reliably, not just at code merge.
Evaluation engineering means automated test suites that catch hallucinations, drift and silent regressions before they reach production users. It is the non-negotiable that separates deployed AI from demo-grade AI — on one engagement the evaluation suite caught a regression introduced by a model-provider update before it ever reached customers.
Yes. Compliance-scoped engagements run under SOC 2 Type II-aligned controls, with additional frameworks such as PIPEDA, OSFI and GDPR as required, customer-specific DPAs, BAAs and MSAs, and audit logging with human-in-the-loop checkpoints.
Ready to Ship Without the Talent War?
Tell us your deployment, your stack and your success metric, and we'll come back with a directional read on your line of business and a scoped proposal — starting with a free 30-minute strategy session.