🔔 Coming Soon — Register Your Interest
AI Engineering
— Build Real AI Products
A comprehensive 1-year live training program covering LLMs, RAG systems, AI agents, MLOps, vector databases, and production AI deployment. Built for engineers who want to design, build, and ship AI-powered products — not just use AI tools.
AI Engineering
Build Real AI Products
📅 1 Year · 240+ hours live
💻 Live Online — Weekday & Weekend
👥 Max 25 Students per batch
🎓 B.E./B.Tech/BCA/MCA Graduates
🏆 Industry Certificate included
🗓️ Launch date TBA — Register now
🚀 This course is in development.
Register your interest and get priority access + early bird pricing when we launch.
What's Coming
AI Engineers Are the Most In-Demand Role in Tech Right Now
Everyone is talking about AI. Very few know how to actually build with it at a production level. AI Engineers bridge the gap between AI research and real products — they design systems, integrate LLMs, build RAG pipelines, deploy AI agents, and keep it all running reliably in production.
1 Year
Program Duration
240+ Hours
Live Instruction
Max 25
Students Per Batch
Production
AI Systems Focus
What You'll Build
Enterprise RAG System
Document ingestion pipeline → vector storage → intelligent retrieval → LLM response generation. Deployed on AWS with API layer.
Autonomous AI Agent
Multi-step reasoning agent with tool use — web search, code execution, database queries. Built with LangChain and deployed as a REST API.
Production AI Application
Full-stack AI product with React frontend, FastAPI backend, LLM integration, monitoring, rate limiting and cost controls in production.
Curriculum Preview
What You'll Learn — Module Overview
12 modules taking you from Python and ML fundamentals all the way to deploying and monitoring production AI systems used by real users.
Module 01
Python & ML Foundations for AI Engineers
- Python for AI — NumPy, Pandas, data manipulation at scale
- ML fundamentals — supervised, unsupervised, evaluation metrics
- Neural network basics — what they are, how they train
- Transformers architecture — attention, embeddings, tokenization
- Hands-on with HuggingFace Transformers library
Module 02
Large Language Models — Deep Dive
- How LLMs work — training, RLHF, instruction tuning
- GPT-4, Claude, Gemini, Llama — comparing capabilities
- Prompt engineering — zero-shot, few-shot, chain-of-thought
- Advanced prompting — ReAct, Tree of Thought, structured output
- LLM evaluation — benchmarks, human eval, automated testing
Module 03
LangChain & LlamaIndex — AI Application Frameworks
- LangChain architecture — chains, memory, callbacks, LCEL
- LlamaIndex — data connectors, query engines, retrievers
- Building conversational AI with memory management
- Structured output parsing and function calling
- LangSmith — tracing, debugging and evaluation
Module 04
Vector Databases & Embeddings
- Embeddings — semantic meaning, similarity search, use cases
- Vector databases — Pinecone, Chroma, Weaviate, pgvector
- Indexing strategies — HNSW, IVF, hybrid search
- Embedding models — OpenAI, Cohere, open-source alternatives
- Optimizing for retrieval quality and latency
Module 05
RAG Systems — Retrieval Augmented Generation
- RAG architecture — ingestion, retrieval, augmentation, generation
- Document processing — loaders, chunking strategies, metadata
- Advanced RAG — re-ranking, HyDE, multi-query retrieval
- Evaluating RAG — faithfulness, answer relevancy, context recall
- Production RAG patterns — caching, fallbacks, observability
Module 06
AI Agents & Tool Use
- Agent architecture — planning, tool use, memory, reflection
- Tool design — function calling, API integration, code execution
- Multi-agent systems with LangGraph and CrewAI
- Autonomous agents — web browsing, file management, databases
- Safety, guardrails and controlling agent behaviour
Module 07
Fine-Tuning & Model Customisation
- When to fine-tune vs prompt vs RAG — decision framework
- LoRA and QLoRA — parameter-efficient fine-tuning
- Dataset preparation and training pipelines
- Fine-tuning open-source models — Llama, Mistral, Phi
- Evaluating and deploying fine-tuned models
Module 08
Project 1 — Enterprise RAG System
- Full RAG pipeline — PDF/web ingestion → Pinecone → LLM
- FastAPI backend with authentication and rate limiting
- React chat interface with streaming responses
- Evaluation suite measuring retrieval and generation quality
- Deployed on AWS with monitoring and cost tracking
Module 09
MLOps — Deploying AI in Production
- Model serving — FastAPI, BentoML, TorchServe, vLLM
- Containerisation with Docker — AI-specific patterns
- CI/CD for AI — GitHub Actions, model versioning, rollback
- Infrastructure — AWS SageMaker, GCP Vertex AI, modal.com
- Scaling inference — batching, caching, load balancing
Module 10
AI Observability & Cost Management
- LLM observability — LangSmith, Helicone, Arize Phoenix
- Monitoring latency, token usage, error rates in production
- Cost optimisation — model selection, caching, batching
- Drift detection and model performance monitoring
- Incident response for AI systems
Module 11
Project 2 — Autonomous AI Agent
- Multi-step agent with web search, code execution and DB tools
- LangGraph for complex multi-agent orchestration
- Human-in-the-loop — approval flows and intervention points
- Full observability with tracing and cost tracking
- Live demo, peer review and instructor feedback session
Module 12
Interview Prep, Resume & Mock Interviews
- 3 full mock technical interviews with detailed feedback
- Common AI Engineer interview questions and system design
- ATS-optimised resume and LinkedIn profile workshop
- GitHub portfolio — all projects hosted and documented
- Job search strategy targeting AI-first companies
Why AI Engineering
The Role That Didn't Exist 3 Years Ago — Now the Most Sought-After in Tech
AI Engineering is not about becoming a researcher. It's about knowing how to integrate, deploy, and scale AI systems that solve real business problems. Every product company — from startups to MNCs — is hiring AI Engineers right now.
Highest Salaries in the Industry
AI Engineers at product companies earn ₹15–40 LPA in India. Senior roles and remote positions for US companies command even more.
Demand Far Exceeds Supply
Every company is building AI features. Engineers who can implement LLMs, build RAG systems and deploy AI agents are extremely rare and highly valued.
Future-Proof Career
AI Engineering is not a trend — it's the direction all of software engineering is moving. Getting in early means you build expertise while the field is still being defined.
Global Remote Opportunities
AI Engineers work remotely for companies in the US, EU and Singapore. The skill set is globally portable — your location stops mattering.
✅ Who This Program Is For
- Software engineers wanting to transition into AI roles
- Fresh graduates with Python knowledge targeting AI companies
- Java/Python Full Stack developers adding AI to their stack
- Backend developers curious about building LLM-powered products
- Anyone who wants to build real AI products — not just prompt ChatGPT
✅ Prerequisites
Solid Python programming skills required. Basic understanding of REST APIs helpful. No prior ML or AI experience needed — we start from foundations.
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