FDE Rajandar

Editor, Art Director, and Web Developer in Hyderabad

Call / WhatsApp: 7993762900.Forward Deployed Engineer (FDE) Training in Hyderabad | GenAI, RAG & Agentic AI | Real-Time Live Projects

A Forward Deployed Engineer (FDE) is a technical professional who works closely with customers or business teams to design, build, integrate, deploy, and customize real-world software and AI solutions.Modern FDE roles increasingly involve building production-ready AI applications, including AI copilots, RAG systems, multi-agent workflows, enterprise assistants, and AI-powered automation.Python → GenAI → LLMs → RAG → AI Agents → Enterprise AI → Deployment → Customer Solutions → FDE

Forward Deployed Engineer (FDE) Course Content

1. Python for AI Engineering

  • Python fundamentals & advanced Python
  • OOP, APIs, JSON, async programming
  • NumPy, Pandas and data processing
  • Git & GitHub
  • Virtual environments and package management

2. Software Engineering for FDE

  • REST APIs and microservices
  • FastAPI development
  • Backend architecture
  • Authentication & authorization
  • Error handling, logging and testing
  • Database integration: SQL / PostgreSQL
  • API integration with third-party services

3. Generative AI Foundations

  • AI, ML & Deep Learning fundamentals
  • LLM concepts and architectures
  • Transformers
  • Tokens, embeddings and context windows
  • Prompt engineering
  • Structured outputs and function calling
  • LLM API integration

4. RAG — Retrieval-Augmented Generation

  • RAG architecture
  • Document processing and chunking
  • Embeddings
  • Vector databases
  • Semantic search
  • Metadata filtering
  • Hybrid search
  • Reranking
  • Advanced RAG
  • RAG evaluation and optimization

5. AI Agents & Agentic AI

  • AI Agent fundamentals
  • Agent architectures
  • Tool calling
  • Function calling
  • Planning and reasoning workflows
  • Multi-agent systems
  • Memory and state management
  • Human-in-the-loop workflows
  • Agent evaluation and observability

6. Agentic AI Frameworks

  • LangChain
  • LangGraph
  • LlamaIndex
  • Agent orchestration
  • Workflow-based agents
  • MCP and tool integrations
  • Building production-ready AI agents

7. Multimodal & Open-Source AI

  • Text, image, audio and document AI
  • Vision-language models
  • Open-source LLMs
  • Model selection and deployment
  • Fine-tuning fundamentals
  • Hugging Face ecosystem

8. Enterprise AI Applications

  • Enterprise chatbots
  • AI copilots
  • Knowledge assistants
  • Customer-support agents
  • Document intelligence
  • SQL/data-analysis agents
  • Internal enterprise search
  • AI workflow automation

9. Cloud, Docker & Deployment

  • Docker
  • CI/CD fundamentals
  • Cloud deployment
  • API deployment
  • Environment and secret management
  • Scaling AI applications
  • Monitoring and logging

10. FDE Skills

  • Customer-focused AI solution development
  • Understanding business requirements
  • Rapid prototyping
  • Building proof-of-concepts
  • Integrating AI into existing applications
  • Debugging production AI systems
  • Working with engineering and business teams
  • Solution architecture and technical communication

11. Testing & Evaluation

  • LLM application testing
  • Prompt testing
  • RAG evaluation
  • Agent evaluation
  • Accuracy and hallucination testing
  • Automated evaluation
  • Performance and reliability testing
  • AI safety and guardrails

12. Real-Time Live Projects

Students can work on end-to-end projects such as:

  1. Enterprise RAG Knowledge Assistant
  2. Agentic Customer Support System
  3. Multi-Agent Research Platform
  4. AI SQL/Data Analyst Agent
  5. Document Intelligence & Automation System
  6. Enterprise AI Copilot
  7. Multimodal AI Assistant
  8. Production-ready Agentic AI Application