Forward Deployed Engineer (FDE) Level 2 – Enterprise Delivery
The Forward Deployed Engineer (FDE) L2 – Enterprise Delivery program is a 28–30 hour training, delivered by Xebia, for engineers with 2–4 years of production experience or L1 graduates who are ready for enterprise-scale technical ownership and direct customer delivery.
The program builds the capability to connect large language models and agentic workflows safely into enterprise ERP, CRM and legacy backend systems, with production-grade operations, security and governance.
Classroom delivery is followed by Phases 1 and 2 of the Deployment Ramp, moving participants from technical ownership of integration modules to co-owned, client-facing POCs and pilots.
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?
• Enterprise Architecture (8–10h)
Domain-driven design, event-driven and API-first/cloud-native architecture, enterprise integration patterns, scalability/resiliency, hybrid/multi-cloud and cost-aware design; AI platform design, multi-agent architecture, MCP, enterprise RAG, knowledge engineering, agent orchestration, evaluation frameworks, and AI observability.
• AI Engineering & Solution Patterns (8h)
Advanced agent workflows, multi-agent collaboration, Semantic Kernel, agent memory, context engineering, prompt orchestration, and human-in-the-loop systems; applied to enterprise copilots, knowledge platforms, enterprise search, AI DevOps assistants, incident response, and workflow automation agents — with reusable components contributed to the FDE Acceleration Toolkit.
• Integration & Quality Engineering (8h)
API management, event streaming, enterprise IAM, secure API engineering, ERP/CRM/legacy backend integration, workflow automation, and integration governance; AI system and RAG evaluation, agent testing, AI risk validation, contract testing, synthetic test data, and performance/regression/production readiness testing.
• Platform Engineering, MLOps & Operations (4–5h)
Enterprise CI/CD and GitOps, containerization and secrets management, progressive delivery, infrastructure automation, multi-environment release management, observability and distributed tracing, MLOps, and incident response.
• Deployment Ramp — Phases 1–2 (engagement-based)
Phase 1: technical ownership of integration modules within an active engagement team. Phase 2: co-owned client-facing POCs and pilots, with documented readiness gates at each transition.
Key takeaways
- Architect enterprise-ready solutions applying modern architecture patterns, AI capabilities and integration strategies.
- Engineer and integrate intelligent enterprise applications across cloud platforms, APIs, ERP and CRM systems and enterprise messaging.
- Validate enterprise solutions through structured quality engineering, AI evaluation frameworks and production readiness assessment.
- Apply enterprise platform engineering practices for deployment, observability, MLOps and continuous optimization.
- Own client-facing POCs and production pilots, leading discovery sessions and solution demonstrations.
Program
The program is structured into seven capability areas totalling 28–30 classroom hours, each combining enterprise concepts with hands-on engineering, followed by two engagement-based phases of the Deployment Ramp.
- Applied Practice• Enterprise Architecture (8–10h)
Domain-driven design, event-driven and API-first/cloud-native architecture, enterprise integration patterns, scalability/resiliency, hybrid/multi-cloud and cost-aware design; AI platform design, multi-agent architecture, MCP, enterprise RAG, knowledge engineering, agent orchestration, evaluation frameworks, and AI observability. - AI Engineering & Solution Patterns (8h)
Advanced agent workflows, multi-agent collaboration, Semantic Kernel, agent memory, context engineering, prompt orchestration, and human-in-the-loop systems; applied to enterprise copilots, knowledge platforms, enterprise search, AI DevOps assistants, incident response, and workflow automation agents — with reusable components contributed to the FDE Acceleration Toolkit. - Integration & Quality Engineering (8h)
API management, event streaming, enterprise IAM, secure API engineering, ERP/CRM/legacy backend integration, workflow automation, and integration governance; AI system and RAG evaluation, agent testing, AI risk validation, contract testing, synthetic test data, and performance/regression/production readiness testing. - Platform Engineering, MLOps & Operations (4–5h)
Enterprise CI/CD and GitOps, containerization and secrets management, progressive delivery, infrastructure automation, multi-environment release management, observability and distributed tracing, MLOps, and incident response. - Deployment Ramp — Phases 1–2 (engagement-based)
Phase 1: technical ownership of integration modules within an active engagement team. Phase 2: co-owned client-facing POCs and pilots, with documented readiness gates at each transition.
Who is it for?
– FDEs who have completed L1, or engineers with 2–4 years of production experience.
– Engineers ready for enterprise-scale technical ownership across ERP, CRM, and legacy backend systems.
– Practitioners who deliver client-facing POCs and production pilots at execution level.
Prerequisites
Level 1 of our FDE Program completion, or equivalent hands-on production experience
Comfort with direct customer interaction at execution level.
Why should I do this training?
Built for production, not demos
Every capability area ends at production readiness. That means observability, MLOps, security validation and operational acceptance, not a working demo.
Built for real enterprise estates
LLMs and agentic workflows are integrated into ERP, CRM and legacy backends under real governance and identity constraints.
Ownership on live engagements
A 3–6 month shadowing program 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 L2 – Enterprise Delivery is the second stage of Xebia’s Forward Deployed Engineer program. It moves engineers from FDE L1’s rapid-prototyping into enterprise-scale technical ownership, then on to FDE L3’s engagement leadership.
Forward Deployed Engineer Level 1 – Foundations
Start with a 48–55 hour program for high-aptitude campus hires and junior developers ready to work directly with enterprise customers.
Start with the Foundations
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 L2 – Enterprise Delivery program prepares engineers to own enterprise-scale technical delivery and client-facing execution. Talk to us about running it for your organization.
