Description

AI FDE Course: The Forward Deployed Engineer & Agentic AI Architect. This course is a comprehensive, practical, three-week roadmap to becoming a Forward Deployed Engineer and Agentic AI Architect, teaching the skills to design, develop, secure, and implement enterprise AI systems. The Forward Deployed Engineer (FDE) job is one of the most exciting and in-demand jobs in AI. Job opportunities in this field have grown by 1,000%, with salaries reaching up to $400,000, and there are approximately 30,000 active job openings in the fields of AI Architecture, Agentic Architecture, and FDE. This course is a complete, three-week roadmap to learning the skills needed to enter this job market. This course covers all the current topics in AI through practical workshops, educational content, and applied projects:

• Harness Engineering
• Local Inference, free models and decision models like Jev
• Agent Teams, Swarms, and Fleets
• Claude Agent SDK and LangChain Deep Agents
• ADLC (AI-Native SDLC)
• Recursive Self-Improvement

By reviewing over 300 FDE job descriptions, the most key skills employers need are included in this course:

• Durable State and Human in the Loop with LangGraph
• AI Gateways and Enterprise Context Layers
• Responsible AI includes security and guardrails
• Running Agents in Production on AWS Bedrock AgentCore
• Evaluations and Observability to deliver measurable results to customers

Over the course of 3 weeks, a commercial product based on Agent Swarms will be developed using LangGraph and deployed on AgentCore. This project is an Enterprise Grade Capstone Project. Free options for AI APIs are available, and there is no mandatory fee. This course is suitable for people of all levels. For beginners, the first week will be an intensive course, and for experienced AI engineers, advanced topics will be introduced quickly.

What you will learn

• Identifying AI Architect and Forward Deployed Engineer (FDE) skills along with a roadmap to achieving them
• Accelerate project delivery with Claude Code and Agentic Development Lifecycle (ADLC) while maintaining accountability
• Using Harness Engineering to Build Agents with LangChain, Deep Agents, and Claude Agent SDK
• Choosing between Frontier, Open-Source, and Local LLMs models based on intelligence, speed, token cost, and privacy
• Using Evals and Observability in Langfuse to guide agent architecture decisions and measure business implications
• Granting long-term memory to agents with Agentic RAG on MCP using Vector Datastores
• Delivering Production-Grade Agent Workflows in LangGraph with Durable State, Crash Recovery, and Human in the Loop
• Scalability and Optimization with Agent Swarms, LLM as a Judge, and Recursive Self-Improvement
• Leading AI initiatives for clients from Discovery and metrics definition to Governance, Responsible AI, and project delivery
• Securing agents against Prompt Injection and security risks using Guardrails, policies, and AI Gateways
• Create data architecture and integration for agents with Enterprise Context Layer and MCP Gateway
• Deploy LangGraph agents in production with AWS Bedrock AgentCore Runtime, Gateway, Policy, and Memory

This course is suitable for people who:

• People looking to explore and enter the AI ​​Forward Deployed Engineer, AI Architect, or Agentic Architect career path.
• Professionals who intend to use AI and Agents to solve real-world problems in a measurable way.
• Those interested in Harness Engineering, Agentic RAG, Agent Swarms, Durable State, Context Layers, and Agent Runtimes.

AI FDE Course Details: The Forward Deployed Engineer & Agentic AI Architect

• Publisher : Udemy
• Teacher : Ed Donner
• Language: English
• Level : All Levels
• Lectures : 124
• Duration : 21 hours and 25 minutes

Course headings

Prerequisites for the AI ​​FDE course: The Forward Deployed Engineer & Agentic AI Architect

Course images

Sample course video

Download link

Download Part 1 – 6 GB

Download Part 2 – 6 GB

Download Part 3 – 6 GB

Download Part 4 – 6 GB

Download Part 5 – 6 GB

Download Part 6 – 6 GB

Download Part 7 – 6 GB

Download Part 8 – 6 GB

Download Part 9 – 4.53 GB

File(s) password: www.downloadly.ir

File size

52.53 GB