Forward Deployed Engineer (FDE) Live — Batch 2
Bridge the gap between AI notebooks and production-grade SLAs. Master high-throughput model serving, enterprise RAG, token FinOps, and agentic guardrails with 2 hours of weekly live instructor immersion.
The world isn't waiting.
Neither should you.
*Based on industry reports from Gartner, McKinsey, and World Economic Forum 2025–2026.
of Fortune 500 companies are piloting multi-agent AI systems
of AI leaders say agentic AI is their top investment priority in 2026
demand-supply gap for certified agentic AI professionals
Over 80% of enterprise AI initiatives fail in production. While self-paced video courses provide foundational theory, mastering complex AI infrastructure—like high-throughput model serving (vLLM, TensorRT-LLM), enterprise RAG, token FinOps, and agentic guardrails—demands real-time interaction, live architecture reviews, and direct expert guidance.
Following the sold-out Founding Cohort, nominations are now open for Batch 2 of the Certified Forward Deployed Engineer (CFDE) Live program.
This premium live variant pairs our industry-standard self-paced curriculum and hands-on lab environment with 2 hours of live, instructor-led weekend immersion every week. Designed for engineers and architects who want to bridge the gap between AI notebooks and production-grade SLAs, this cohort provides direct access to senior AI architects, live code debugging, and real-time solution design breakdowns.
Live Weekend Immersion: 2 hours of weekly live instructor-led sessions, live code teardowns, and architectural reviews.
Production-Grade Tech Stack: Hands-on mastery of vLLM, TensorRT-LLM, Enterprise RAG, LangGraph/AutoGPT, and Token FinOps.
Direct Mentor Access: Interactive architecture reviews and real-time debugging with senior AI infrastructure leaders.
Exclusive Peer Cohort: Candidate-screened profile selection to maintain high-level technical discussions and peer learning.
Industry Credential: Earn the Certified Forward Deployed Engineer (CFDE) designation backed by AIM Media House / ADaSci.
High-Throughput Model Serving: Architect and deploy scalable inference engines using vLLM and TensorRT-LLM under strict latency SLAs.
Enterprise-Grade RAG Systems: Build hybrid search, reranking, and chunking pipelines optimized for high retrieval accuracy and security.
Token FinOps & Routing: Implement cost-optimization, semantic caching, and dynamic model routing to minimize enterprise LLM operational costs.
Agentic Orchestration & Guardrails: Design stateful multi-agent workflows (LangGraph/CrewAI) with real-time safety, hallucination checks, and execution guardrails.
Production Reliability & Monitoring: Deploy end-to-end evaluation frameworks, tracing (LangSmith/Phoenix), and CI/CD pipelines for LLMs.
AI/ML Engineers & Software Engineers moving from notebook prototypes to production AI systems.
Backend & Cloud Architects responsible for scaling enterprise LLM infrastructure and maintaining SLAs.
Technical Leads & Engineering Managers evaluating and building enterprise agentic workflows and guardrails.
Data Scientists looking to gain core Forward Deployed Engineering skills to deploy production-ready models.
Proficiency in Python and standard data/backend engineering libraries.
Basic understanding of machine learning concepts and LLM API integrations (e.g., OpenAI, Anthropic, or Hugging Face).
Familiarity with REST APIs, Docker/containers, and basic cloud computing environments (AWS, GCP, or Azure).
Prior experience in AI/ML is helpful, but deep production experience with LLM serving is NOT required—that is what this program teaches.
Full Access to Live Cohort Sessions: Weekly 2-hour interactive sessions, session recordings, and annotated architecture notes.
Self-Paced Curriculum Vault: Lifetime access to comprehensive self-paced video modules, code repositories, and reference architectures.
Production Deployment Blueprints: Downloadable deployment scripts, Docker files, and enterprise RAG templates.
CFDE Certificate of Completion: Digital badge and verified credential issued by ADaSci upon passing the final capstone evaluation.