Generative AI & LLM Engineering
Go from calling an LLM API to shipping evaluated, observable, production-grade AI systems. A 16-week, project-based program for fresh IT graduates and working professionals who want to build — not just talk about — RAG pipelines, AI agents, MCP integrations, fine-tuned models and LLMOps platforms.
The Generative AI & LLM Engineering program is a live, instructor-led course that takes you from calling an LLM API to shipping evaluated, observable, production-grade AI systems. Every module is built around something you ship: a RAG pipeline with citations, an agent that uses your tools over MCP, an eval harness running in CI, a fine-tuned model that beats its base — not slides about them.
It is designed for two audiences at once. If you are a fresh IT graduate, it gives you a portfolio of deployed AI systems that speaks louder than any resume line. If you are a working professional — backend, full-stack, data or ML — it converts the engineering skills you already have into the AI engineering skills the market is hiring for right now.
The program runs 16 weeks (~120 hours), live online and project-based, and closes with a capstone reviewed 1:1: you design, build, evaluate, deploy and defend one production-grade agentic AI application end-to-end.
Course at a Glance
- Duration: 16 weeks | ~120 hours | live + hands-on
- Format: Live online · project-based · capstone reviewed 1:1
- Prerequisite: Working Python. No ML/maths background required.
- Curriculum: 12 modules, from transformer foundations to LLMOps and capstone
- Portfolio: 6 production-style projects plus a defended capstone
Course Highlights
- Every module leads with what you build, not what you "learn about"
- Production RAG: hybrid search, re-ranking, GraphRAG, chunk-level citations
- Agentic AI with LangGraph, CrewAI and the Model Context Protocol (MCP)
- Evaluation-first engineering: eval sets, LLM-as-judge, regression suites in CI
- Fine-tuning with LoRA/QLoRA and DPO — and knowing when not to fine-tune
- AI safety and security aligned to the OWASP Top 10 for LLM applications
- LLMOps: vLLM serving, caching, autoscaling, CI/CD for prompts and models
Hands-on Projects & Portfolio
Multi-source RAG assistant over enterprise PDFs with hybrid search, re-ranking and inline citations.
An MCP server exposing internal tools to a customer-facing agent through one standard interface.
A research-and-report system built on LangGraph with planning, tool use and human-in-the-loop.
A domain-specific fine-tuned model, benchmarked head-to-head against the base model.
Nightly regression runs with a hallucination-rate dashboard wired into CI.
Document processing at scale using model routing and prompt caching to cut spend.
Tools & Stack
Python PyTorch Hugging Face OpenAI Anthropic Claude Gemini Llama LangChain LangGraph LlamaIndex CrewAI MCP pgvector Qdrant Pinecone Weaviate Neo4j RAGAS Langfuse vLLM FastAPI Docker AWS Bedrock
Career Outcomes
Demand for applied AI engineers who can ship RAG plus eval pipelines is growing far faster than the supply of engineers who can build them. This course targets the roles at the centre of that gap:
- AI Engineer
- LLM Engineer
- Applied AI Engineer
- RAG Developer
- Agentic AI Engineer
- AI Platform Architect
- MLOps / LLMOps Engineer
Who This Course Is For
- Final-year students and fresh IT graduates targeting an AI-first first role
- Software / backend / full-stack engineers converting existing skills into AI engineering
- Data analysts, data scientists and ML engineers moving into LLM and agent systems
- Tech leads and architects who need to make build-vs-buy calls on GenAI systems
Material Includes
- Class recordings
- Lab guides & project starter repos
- Eval datasets & grading rubrics
- Certificate
Prerequisites
- Working Python
- No ML or maths background required
- A laptop and roughly 8 hours a week
Estimated Time: 8 Hours
You build:- A model-selection playbook for a real business task — token-level cost analysis, context-window budgeting, and a scored comparison across model families
- How LLMs are actually trained; tokenization, context windows, attention intuition
- Model families: GPT, Claude, Gemini, Llama, DeepSeek, Mistral
- Open vs closed trade-offs; choosing a model for a job
Estimated Time: 8 Hours
You build:- A versioned prompt library with structured JSON-schema outputs and an automated prompt regression test suite
- System prompts as versioned software contracts; zero/few-shot, chain-of-thought
- Structured outputs and JSON schemas; XML-tag structuring
- Prompt caching; prompt regression testing
Estimated Time: 10 Hours
You build:- A streaming, tool-calling LLM service with retries and backoff, token budgeting, and cheap-model/strong-model routing
- Streaming; function/tool calling; multimodal and vision inputs
- Retries and backoff; token budgeting; latency optimization
- Model routing: cheap model to extract, strong model to reason
Estimated Time: 8 Hours
You build:- A semantic search service over your own corpus with tuned chunking, metadata filtering, and index benchmarking on pgvector and Qdrant
- Embedding models; chunking strategies; similarity search
- pgvector, Qdrant, Pinecone, Weaviate
- Metadata filtering; index tuning
Estimated Time: 12 Hours
You build:- A multi-source RAG assistant over enterprise PDFs with hybrid search (BM25 + dense), re-ranking, query rewriting, and chunk-level citations
- Naive vs advanced RAG; contextual retrieval
- Hybrid search (BM25 + dense); re-ranking; query rewriting
- GraphRAG with Neo4j; handling tables and PDFs; chunk-level citations
Estimated Time: 12 Hours
You build:- A multi-agent research-and-report system on LangGraph with planning, tool use, memory, and human-in-the-loop checkpoints
- ReAct loops; planning; tool use; memory
- LangGraph, CrewAI, LlamaIndex
- Multi-agent patterns and their failure modes; long-running/durable agents
Estimated Time: 8 Hours
You build:- A custom MCP server that exposes internal tools to a customer-facing agent, with sandboxed execution via E2B/Modal
- MCP servers and clients; A2A
- Exposing internal tools to agents; sandboxed execution (E2B/Modal)
- Replacing bespoke integration layers with a standard interface
Estimated Time: 12 Hours
You build:- An automated eval harness with nightly regression runs, a calibrated LLM-as-judge, and a hallucination-rate dashboard traced end-to-end
- Building eval sets from day one; RAGAS; retrieval precision/recall
- LLM-as-judge and judge calibration; regression suites in CI
- Tracing and monitoring with Langfuse / Phoenix / Helicone; cost and latency dashboards
Estimated Time: 10 Hours
You build:- A domain-specific model fine-tuned with LoRA and quantized for serving, benchmarked head-to-head against the base model
- When not to fine-tune; SFT; LoRA/QLoRA; PEFT
- DPO and preference data; Hugging Face pipelines
- Quantization; evaluating a fine-tune against the base model
Estimated Time: 8 Hours
You build:- A guardrail layer for an existing agent — prompt injection defense, PII redaction, output validation, and an audit trail — tested against the OWASP LLM Top 10
- OWASP Top 10 for LLM applications
- Prompt injection and indirect injection defense; jailbreak resistance
- PII redaction; content filtering; output validation; responsible AI and audit trails
Estimated Time: 12 Hours
You build:- A containerized, autoscaled AI service — FastAPI + vLLM with caching layers, CI/CD for prompts and models, and a cost-control dashboard
- Containerizing AI services; FastAPI serving; vLLM and inference optimization
- Caching layers; autoscaling; cost control at scale
- CI/CD for prompts and models; on-call for non-deterministic systems
Estimated Time: 12 Hours
You build:- One production-grade agentic AI application, end-to-end: designed, built, evaluated, deployed — and defended in a 1:1 review
- Architecture design review and scoping
- Evaluation plan, deployment, and observability
- 1:1 capstone defense
- What's App: +91 73-01-11-21-31 for immediate assistance during business hours.
- Email: Send your inquiries to hello@progmatrix.com, and one of our representatives will get back to you as soon as possible.
- Website: You can also use the contact form on our website to send us a message directly.