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Generative AI Engineering & Development

3 Months

Course Information

Certified Generative AI Engineer (Developer Track)

Comprehensive coverage of Generative AI Engineering. Learn the core concepts of Large Language Models (LLMs), Agentic workflows, and Retrieval-Augmented Generation (RAG). Build real-world AI applications using Python, LangChain, and Vector Databases with robust backend functionality.

Understand the architecture of Multi-Agent Systems to orchestrate teams of AI workers that collaborate on complex tasks. Master the implementation of Agentic workflows using tools like n8n and LangGraph to automate code generation and deployment.

Master production deployment, security, and MLOps. You will learn to containerize your AI applications with Docker, implement evaluation frameworks, and deploy secure, scalable AI solutions ready for enterprise use.

What will you Learn ?

  • LLM Application Development: Build ChatGPT-level applications using Python, OpenAI, Groq, and Streamlit with advanced prompt engineering systems.
  • Advanced RAG Systems: Create "Project Atlas"—your AI second brain—using Vector Databases (LanceDB) to make AI understand private data.
  • Autonomous Agents: Build "Project Orion" and multi-agent teams capable of writing code, raising GitHub PRs, and executing workflows autonomously.
  • Production Deployment: Deploy secure, monitored AI systems using Docker, FastAPI, and evaluation tools like LangSmith and DeepEval.

Curriculum

Level 1: Engineering Foundations & LLM Architecture

Focus: Python mastery, Open-Source Models, and building UI-based AI apps.

  • Core Modules:
    • Python Basecamp: Advanced data structures, Async functions, and Environment setup.
    • LLM Fundamentals: Transformers, Embeddings, and managing Context Windows.
    • Open Source & Local AI: Running Mistral/Llama 3 locally using Ollama and GGUF quantization.
    • Advanced Prompting: Implementing CoT, Few-Shot, and JSON-structured outputs.
    • Streamlit Application Dev: Building Chat UIs, Session State management, and streaming responses.
  • Tools Stack:
    • Python, OpenAI API, Ollama, Groq, Streamlit.
  • Key Project:
    • Build a "Multi-Model Chatbot" with a full Streamlit UI.
Level 2: Advanced RAG & Vector Database Systems

Focus: Engineering systems that "read" proprietary data.

  • Core Modules:
    • RAG Architecture: Retrieval vs. Generation pipelines and Chunking strategies.
    • Vector Databases: Implementing LanceDB and Pinecone.
    • LlamaIndex Mastery: Data ingestion pipelines and Query Engines.
    • Hybrid Search: Keyword + Semantic search.
  • Tools Stack:
    • LlamaIndex, LanceDB, Hugging Face Embeddings, PyPDF.
  • Key Project:
    • "Project Atlas": Enterprise Knowledge Bot from PDFs.
Level 3: Agentic Workflows & Automation

Focus: Integrating AI with external tools using n8n and MCP.

  • Core Modules:
    • n8n Automation: Webhooks, JSON transformations, and integrations.
    • MCP: Connecting AI with local databases and APIs.
    • Function Calling: Teaching LLMs to execute Python & APIs.
    • Tool Use: File I/O, Web Search, Calculations.
  • Tools Stack:
    • n8n, Claude Desktop, MCP SDK, Composio.
  • Key Project:
    • "Auto-Job Hunter": AI job search workflow.
Level 4: Multi-Agent Orchestration & Logic

Focus: Building teams of specialized AI agents.

  • Core Modules:
    • LangChain & LangGraph: Graphs, State management, Manager-Worker systems.
    • Text-to-SQL Agents: Safe SQL querying.
    • Coding Agents: Write, debug, refactor code.
    • Memory Systems: Short-term vs Long-term memory.
  • Tools Stack:
    • LangChain, LangGraph, LangSmith, SQL DBs.
  • Key Project:
    • "Project Orion": Multi-Agent Dev Team.
Level 5: Multimodal AI & Voice Engineering

Focus: Voice, Audio, and Vision AI systems.

  • Core Modules:
    • Voice AI Engineering: STT → LLM → TTS pipelines.
    • Computer Vision: CLIP and Diffusion models.
    • Multimodal Pipelines: AI that sees & speaks.
    • Latency Optimization: Sub-second voice responses.
  • Tools Stack:
    • VAPI, ElevenLabs, OpenAI Vision, Diffusers.
  • Key Project:
    • "Voice Support Bot": Real-time voice assistant.
Level 6: MLOps, Security & Production Deployment

Focus: Deploying secure, scalable AI systems.

  • Core Modules:
    • Backend Engineering: FastAPI microservices.
    • Containerization: Docker & Cloud Run.
    • Evaluation: DeepEval accuracy & hallucination testing.
    • Security & Cost: PII masking, rate limiting.
  • Tools Stack:
    • Docker, FastAPI, Google Cloud Run, DeepEval.
  • Key Project:
    • Capstone Deployment – Secure Dockerized AI Microservice.
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Duration
3 Months
Estimated Seat
25
Language
Tamil
Category
AI & Business
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