Forward Deployed Engineer (FDE) Level 1 – Foundation
The Forward Deployed Engineer (FDE) L1 – Foundation program is a 48–55 hour training, delivered by Xebia, for high-aptitude campus hires and junior full-stack or cloud developers who are ready to work alongside enterprise customers as autonomous rapid-prototypers.
The program turns strong coders into Forward Deployed Engineers who can run discovery, frame ambiguous business problems, and build, deploy and demonstrate AI-powered enterprise applications end to end.
Classroom delivery is followed by a Build Sprint and a 3–6 month Shadowing Programme, so capability is proven on live engagements rather than in the classroom alone.
Contact Us for In-company Training
Our in-company training programs are fully customizable to meet your organization’s unique needs. Reach out to discuss how we can help your team grow.
Kyle Anderson
Academy Advisor
What will you learn?
• Technical track — enterprise application and API engineering (REST APIs, OAuth and JWT, third-party integration, webhooks, error handling), solution architecture essentials (layering, microservices, API-first and cloud-native design, scalability), applied AI engineering (LLMs, prompt and context engineering, embeddings, vector databases, RAG, function calling, AI safety and guardrails), building and evaluating AI solutions (AI assistants, knowledge base integration, agent basics, model evaluation, responsible AI), cloud-native deployment and operations (Git, Docker, CI/CD, Azure/AWS/GCP deployment, secrets management, monitoring), and rapid prototyping with the FDE Acceleration Toolkit (deployment boilerplates, infrastructure-as-code, AI-assisted development, reviewing AI-generated code).
• Consulting track — the FDE role and operating model, discovery and problem framing (root cause analysis, requirement discovery, user stories), working with ambiguity and enterprise reality (legacy systems, technical debt, messy data, build vs buy vs integrate), stakeholder and scope management (influence mapping, scope creep, escalation), and client communication and demonstration (live demos, sprint reviews, handling objections).
• Applied practice — a guided capstone build, an independent Build Sprint of two further applications, and a 3–6 month shadowing programme with a Senior FDE or Lead Architect.
Key takeaways
- Engage business stakeholders directly to validate requirements and distinguish business problems from technical symptoms.
- Operate effectively without complete requirement documents, converting ambiguous client input into deliverable technical scope.
- Rapidly prototype and deploy AI-powered enterprise applications, compressing prototype-to-production cycles.
- Recommend enterprise-ready solution approaches considering scalability, security and integration complexity.
- Deploy and operate production-ready solutions on cloud infrastructure with appropriate monitoring and security.
- Communicate technical solutions and trade-offs credibly to non-technical audiences.
- Contribute autonomously within Forward Deployed Engineering teams at client sites.
Program
The program is structured into three tracks totaling 48–55 classroom hours, followed by applied practice on live engagements.
Technical Track and Consulting Track run in parallel; Applied Practice is where both integrate.
- Technical Track — 30–34 hours
◦ Enterprise Application & API Engineering (5h): REST APIs, OAuth/JWT, third-party integration, webhooks, error handling.
◦ Solution Architecture Essentials (4h): application layering, microservices, API-first and cloud-native design, scalability.
◦ Applied AI Engineering (8h): LLMs, prompt and context engineering, embeddings, vector databases, RAG, function calling, guardrails.
◦ Building & Evaluating AI Solutions (6h): AI assistants, knowledge base integration, agent basics, model evaluation, responsible AI.
◦ Cloud-Native Deployment & Operations (5h): Git, Docker, CI/CD, cloud deployment, secrets management, monitoring.
◦ Rapid Prototyping & the FDE Acceleration Toolkit (4h): deployment boilerplates, infrastructure-as-code, AI-assisted development, reviewing AI-generated code. - Consulting Track — 18–21 hours
◦ The FDE Role & Operating Model (3h): delivery lifecycle, customer-centric mindset, success metrics.
◦ Discovery & Problem Framing (5h): root cause analysis, requirement discovery, user stories.
◦ Working with Ambiguity & Enterprise Reality (4h): legacy systems, technical debt, messy data, build vs buy vs integrate.
◦ Stakeholder & Scope Management (3h): influence mapping, scope creep, escalation.
◦ Client Communication & Demonstration (4h): live demos, sprint reviews, handling objections.
◦ Career Development for FDEs (2h): technical portfolios, career progression, emerging trends. - Applied Practice
◦ Sandbox, Build Sprint & Shadowing: guided capstone, two independent applications (4–8 weeks), 3–6 month shadowing with mentor checkpoints.
Who is it for?
– High-aptitude campus hires and junior full-stack or cloud developers with high learning velocity and comfort with ambiguity.
– Engineers moving from pure development into customer-facing enterprise delivery.
– Teams building an internal Forward Deployed Engineering capability from the ground up.
Prerequisites
Working knowledge of at least one programming language, Python preferred.
No prior AI or ML experience required.
Basic Git, SQL and command-line familiarity.
Why should I do this training?
Client fluency, not just code
Track B builds the customer-facing half of the role — discovery, problem framing, stakeholder handling and live demonstrations — in parallel with the technical track.
Build three real applications
A guided capstone plus an independent Build Sprint of two further end-to-end applications, each linked to a real business problem and assessed on deployment readiness.
Proven on live engagements
A 3–6 month shadowing programme pairs every participant with a Senior FDE or Lead Architect, progressing from observing to contributing to co-leading.
What else should I know?
After filling out the form below, you’ll receive a confirmation email, and our team will reach out to understand your team’s needs and goals.
Course information
Duration: 48–55 classroom hours.
Track A 30–34 hours
Track B 18–21 hours — plus 3–6 months of applied practice.
Assessment covers knowledge checks, graded hands-on labs, consulting simulations, three end-to-end applications, and a mentor sign-off against client-readiness criteria.
Reference toolchain: GitHub Copilot, Claude Code, Cursor IDE, OpenAI API, Azure OpenAI, LangChain, LangGraph, FastAPI, Docker, Git, GitHub Actions, Azure App Service, PostgreSQL, pgvector, Postman and VS Code.
In-company and custom delivery options available — contact us to discuss your team’s needs
Continue your path
Empower Your Team with the Skills to Lead in AI
FDE L1 – Foundation is the first stage of Xebia’s Forward Deployed Engineer program. It builds autonomous rapid-prototypers, then continues through FDE L2’s enterprise-scale delivery into FDE L3’s engagement leadership.
Forward Deployed Engineer
Level 2 – Enterprise Delivery
Continue your journey with a 28–30 hour program for L1 graduates or engineers with 2–4 years of production experience, ready for enterprise-scale technical ownership and direct customer delivery.
Explore our Enterprise Delivery
Forward Deployed Engineer
Level 3 – Engagement Leadership
Finalize the path with a 12–14 hour program for L2 graduates, or Solutions Architects and senior engineers with 5+ years of experience, ready to own the business case, technical roadmap, and executive relationship in enterprise engagements.
Become an expertFrequently Asked Questions
What does it look like?
Ready to transform your business?
The FDE L1 – Foundation program prepares strong coders to contribute autonomously within Forward Deployed Engineering teams at client sites. Talk to us about running it for your organization.
