AI Engineering

What You'll Master

✓ Advanced LLM Architecture & Prompt Engineering
✓ RAG Systems, Vector Databases & Retrieval Engineering
✓ Fine-tuning, PEFT (LoRA, QLoRA), Model Quantization & Evaluation
✓ Agentic AI Frameworks (LangGraph, CrewAI, AutoGen)
✓ MLOps & LLMOps: Monitoring, Versioning, CI/CD for AI
✓ Cloud Deployment (AWS Bedrock, Azure AI, GCP Vertex AI, Docker/K8s)
✓ 100% Hands-on + Production-grade Projects + Certificate

Course Snapshot

Duration: 6–9 months (or 4–5 months fast-track)

Level: Intermediate to Advanced (Python + ML basics required)

Format: Live session + Real Enterprise Projects

Certificate: Yes (AI Engineering & LLMOps Specialization)

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Curriculum Overview

Transformer Architecture Deep Dive, LLM Training vs Inference, Tokenization, Attention Mechanisms, Open vs Closed Models, Prompt Engineering Advanced, Evaluation Metrics (BLEU, ROUGE, BERTScore, Human Eval)...

Vector Databases (Pinecone, Weaviate, Chroma, Qdrant), Embeddings (OpenAI, Cohere, Hugging Face), Hybrid Search, Re-ranking, Chunking Strategies, Advanced RAG Patterns (HyDE, Self-RAG, Corrective RAG)...

Full Fine-tuning vs PEFT (LoRA, QLoRA, DoRA), Quantization (4-bit, 8-bit), Model Distillation, Instruction Tuning, Preference Optimization (DPO, PPO), Evaluation Frameworks (LM-Eval, Open LLM Leaderboard)...

LangGraph, CrewAI, AutoGen, LlamaIndex Workflows, Memory Systems, Tool Calling, Reflection & Planning, Multi-Agent Collaboration, Supervisor + Worker Patterns, Dynamic Routing...

MLflow, Weights & Biases, LangSmith, PromptLayer, Phoenix, Arize, Grafana + Prometheus for LLM Monitoring, Rate Limiting, Cost Optimization, A/B Testing, Canary Deployments...

AWS Bedrock, Azure OpenAI, Google Vertex AI, Hugging Face Inference Endpoints, Docker + Kubernetes, FastAPI / Gradio / Streamlit Deployment, Security (PII Detection, Guardrails), 3–4 End-to-End Production Projects (RAG Chatbot, Multi-Agent Customer Support, Fine-tuned Domain Model, Enterprise Analytics Agent)...

What You'll Master

Course Snapshot

Curriculum Overview

Module 1 – Foundations of Modern AI Engineering

Module 2 – Retrieval-Augmented Generation (RAG) Engineering

Module 3 – Fine-tuning & Model Optimization

Module 4 – Agentic & Multi-Agent Systems Engineering

Module 5 – MLOps / LLMOps & Production Systems

Module 6 – Cloud Deployment & Enterprise AI Engineering + Capstone

Ready to Become an AI Engineer?