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Build autonomous AI agents, multi-agent systems and production-grade LLM applications — the frontier skill set that commands the highest salaries in the global AI job market right now.
Delivered by ThoorigAI Infotech in partnership with Elysium Academy
Transformers · Attention · Tokenisation · Fine-tuning
Chain-of-Thought · Few-Shot · System Prompts · GPT-4o
Vector DBs · Embeddings · Retrieval · LangChain / LlamaIndex
LangGraph · CrewAI · AutoGen · Tool Calling · Memory
FastAPI · Docker · Cloud · LLMOps · Monitoring
From LLM fundamentals to autonomous multi-agent systems.
Total Training Duration
Practical Agent Projects
Student Rating
Tech Hiring Partners
Avg. Salary Growth
Every tech company — startups to Fortune 500s — is racing to hire engineers who can build autonomous AI agents and LLM-powered products. The talent gap is enormous.
A complete, hands-on Agentic AI and LLM Engineering education — from transformer architecture fundamentals to building, deploying and monitoring autonomous multi-agent systems
Understand how Large Language Models actually work — attention mechanisms, tokenisation, context windows, model families (GPT, Claude, Gemini, Llama) and the prompt-response lifecycle from the inside out.
Master professional-grade prompting — chain-of-thought, tree-of-thought, few-shot and zero-shot prompting, system prompts, structured outputs and prompt optimisation techniques used by AI product teams.
Build knowledge-grounded LLM applications — vector embeddings, semantic search, Pinecone / ChromaDB / FAISS vector databases, document chunking strategies, re-ranking and hybrid retrieval pipelines.
Build production LLM pipelines with the leading orchestration frameworks — chains, memory, tool use, structured output parsing, LlamaIndex query engines and complex multi-step workflows over real data.
Design and build autonomous AI agents that plan, reason and execute tasks — ReAct agents, tool-calling agents, function calling, LangGraph state machines, CrewAI multi-agent teams and AutoGen workflows.
Deploy LLM applications to production — build APIs with FastAPI, containerise with Docker, deploy to cloud, implement observability with LangSmith, manage costs and monitor agent performance at scale.
This is an advanced, developer-focused programme — designed for anyone with Python experience who wants to build the next generation of AI-powered applications and autonomous systems
Already coding in Python? Agentic AI and LLM Engineering is the single highest-leverage skill you can add right now — this course takes you from developer to AI agent builder in 80 hours.
Already working with ML models? Move into the hottest frontier — agentic systems, RAG pipelines and LLM application development are where the premium roles and highest salaries are.
Launch your career at the bleeding edge of AI — Agentic AI and LLM Engineering roles have massive talent shortages, and graduates with this skill set are being hired immediately.
Bridge from data to AI products — LLM engineering and agent building expands your career into AI product development, the fastest-growing and highest-paid technical discipline in 2026.
22 structured modules — from LLM fundamentals and prompt engineering through RAG, LangChain, autonomous agent frameworks and full production LLMOps deployment
Understanding what separates Agentic AI Engineering from standard AI/ML work — and why it commands the highest salaries in the industry
| Dimension | Agentic AI Engineer This Course | Traditional AI Developer |
|---|---|---|
| What They Build | Autonomous agents that plan, use tools, retrieve knowledge and complete complex multi-step tasks | Trained ML models and data pipelines that require explicit human instruction |
| Core Technologies | LLMs, LangChain, LangGraph, CrewAI, RAG, Vector DBs, Function Calling | Scikit-learn, TensorFlow, PyTorch, ML model training pipelines |
| Autonomy Level | Systems that reason, plan and act independently to achieve goals | Systems that respond to fixed input with fixed learned output |
| Business Value | Automate entire workflows end-to-end — replaces teams of manual workers | Automates a specific prediction or classification task |
| Avg. Salary (India) | ₹15L – ₹40L+ per annum | ₹8L – ₹20L per annum |
| Job Growth (2026) | 70%+ year-on-year — massive talent shortage globally | 25–35% year-on-year |
| Market Stage | Frontier — early movers command enormous premiums right now | Maturing — competitive, established talent pool |
Compensation at each career stage — Agentic AI skills deliver the steepest salary curve of any technical discipline in 2026
Graduate with a RAG-powered research agent, a multi-agent CrewAI workflow and a deployed LangGraph state machine — all real, portfolio-ready and fully functional.
Direct, personalised guidance from practising Agentic AI and LLM engineers working in production AI environments at leading tech companies.
Earn an Agentic AI & LLM Engineering certificate from Elysium Academy — recognised by 400+ tech hiring partners and positioned as a frontier AI credential.
Work with real OpenAI, Hugging Face and vector database APIs throughout the course — every project uses actual production tooling, not sandboxed simulations.
Dedicated sessions on GitHub portfolio setup, AI engineering resume writing, LinkedIn profile optimisation and mock technical interviews for LLM engineering roles.
Continue to access your LLM engineering trainers after the programme for project debugging, architecture reviews and career guidance as you secure your first Agentic AI role.
Agentic AI and LLM Engineering skills unlock the most in-demand, highest-paid and fastest-growing roles in the global tech job market — this course positions you for all of them
Design and build autonomous AI agents using LangGraph, CrewAI and AutoGen for enterprise and product companies
Build LLM-powered products — chatbots, RAG systems, knowledge bases and AI-first SaaS features using LangChain and OpenAI
Specialise in retrieval-augmented systems — build and optimise semantic search pipelines and knowledge-grounded AI applications
Own the deployment, monitoring and optimisation of LLM applications in production — observability, cost management and reliability at scale
Bridge AI engineering and product — build and ship AI-powered features directly used by thousands of end users at product-led companies
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