Master'sinGenAI&AgenticAI
Skill AI's flagship 10-month, 480-hour programme — from Python fundamentals to designing, building and deploying autonomous multi-agent AI systems. Exit having shipped a Company-sourced multi-agent product against a real partner brief.
From Python first-principles to autonomous agents
Every module in the Skill AI Learning Model closes with a Company-assigned project drawn from live partner briefs — no theory-only modules. You move through Python, ML, deep learning, NLP, Generative AI, Agentic AI, MLOps and cloud deployment, and finish with a capstone shipped against a real stakeholder.
Fast Track — 2 hrs/day, 6 days/week for 10 months. Normal Track — same 480 hours delivered over ~20 months at half the weekly pace. Both tracks share the same syllabus, projects and capstone.
Python, Data & Math Foundations for AI
- Python — syntax, OOP, exception handling
- NumPy & Pandas · Linear algebra for AI
- Calculus intuition for ML
- Git, Jupyter, Colab & VS Code
Your 40-week journey
9 modules · 240 sessions · every module closes with a Company-assigned project.
Python, Data & Math Foundations for AI
- Python — syntax, OOP, exception handling
- NumPy & Pandas · Linear algebra for AI
- Calculus intuition for ML
- Git, Jupyter, Colab & VS Code
Statistics, Probability & ML Foundations
- Descriptive stats & hypothesis testing
- Regression, classification, ensembles
- Clustering & dimensionality reduction
- Scikit-learn pipelines · Bias-variance
Deep Learning Foundations (NN, CNN, RNN)
- Neural nets & backpropagation
- Optimisers, regularisation, batch norm
- CNNs for vision · RNN / LSTM / GRU
- PyTorch / TensorFlow · GPU basics
NLP & Transformer Architectures
- Embeddings — word2vec, GloVe, contextual
- Attention & Transformer architecture
- BERT-family fine-tuning; sequence-to- sequence & decoder-only architectures
- Hugging Face · IndicBERT · multilingual
Generative AI & Large Language Models
- Pretraining, instruction tuning, RLHF
- Prompt engineering · CoT · few-shot
- FAISS / Chroma / Pinecone · RAG · GraphRAG
- LoRA, QLoRA, PEFT, DPO, ORPO · Guardrails
Agentic AI Systems & Multi-Agent Orchestration
- Agent anatomy — planning, memory, tools
- LangGraph · CrewAI · AutoGen
- Model Context Protocol (MCP)
- GPT-4o, LLaVA, CLIP, image + text + audio
AI Product Engineering, Evaluation & MLOps
- FastAPI · MLflow · Model registry
- LLM & agent evaluation
- prompt-injection defence , observability — logging
- vLLM / TensorRT-LLM · CI/CD
Cloud AI Infrastructure & Scalable Deployment
- Docker containerisation for AI workloads
- cloud compute for AI — GPU instances
- Managed inference endpoints
- Scaling strategies — load balancing, caching, batching
Capstone — Manager Sourced Multi Agent Product
- Real Partner Brief - Solution Architecture.
- Multi-Agent Product Design & Data Sourcing.
- Build, test & iterate against live feedback.
- Professional Final Demo & Stakeholder Pitch ; portfolio packaging
The end-to-end GenAI toolkit
From notebooks to multi-agent orchestration — the stack used by leading AI product teams.
Where this programme takes you
Every role below maps to specific modules — the programme is designed so you can specialise as you graduate.
Career Outcomes
Design & build production-grade GenAI systems
Ship multi-agent workflows with LangGraph / CrewAI / AutoGen
Fine-tune LLMs with LoRA / QLoRA / PEFT
Architect RAG & GraphRAG on real partner data
Containerise & auto-scale AI services on cloud GPU
Deliver a Company-sourced capstone to a live partner brief
Built for technical & non-technical entrants
The programme takes learners from first principles to deployment — eligibility is based on aptitude and intent, not a specific prior degree.
- No prior coding experience is mandatory — Python is taught from first principles in Module 1.
- Comfort with basic computer operation and logical/analytical reasoning (10+2 or equivalent maths).
- Willingness to commit to the daily schedule and Company Standard project work.
What you'll need on your machine
Deep learning and LLM training in Modules 3–5 run on institute-provided cloud GPU credits — your personal machine doesn't need a dedicated GPU.
- Python 3.10+ and a code editor (VS Code or PyCharm)
- Anaconda or venv/conda for dependency management
- Git locally + GitHub account for version control & portfolio
- Docker Desktop (installed ahead of Module 8)
- Google account for Colab; institute-issued sandbox API keys (Module 5)
- Modern browser (Chrome/Edge) for LMS, Jupyter/Colab and cloud consoles
All heavy training and LLM inference runs on Google Colab Pro / AWS / GCP credits provided by the institute. No personal GPU billing required.
Learners without a personal laptop can use institute lab systems, pre-configured to the recommended spec.
Reserve your seat
Applications are reviewed by our admissions team within 24 hours.