A.I. Mastery
Six months to become the person who builds AI for a living
The flagship fellowship. Six months of structured building followed by six months of continued community, mentorship and placement support. Two tracks run in parallel: the generalist track for people who want to lead AI transformation inside a business, and the engineering track for people who want to build and ship AI products. Everyone graduates with a real portfolio, a capstone shipped to real users, and the vocabulary to hold their own in any AI room.
Who this course is for
- Serious career switchers targeting AI engineer, AI product or AI lead roles
- Senior professionals asked to own the AI agenda for their function or company
- Founders building an AI first product who need depth, not a weekend crash course
- Engineers who want to move from writing features to designing intelligent systems
- Consultants and educators who want to teach and advise credibly
What you will learn
- Master the full stack of modern AI: models, retrieval, agents, evaluation and deployment
- Fine tune and adapt open models, and know when fine tuning beats prompting or retrieval
- Design multimodal systems across text, image, audio and video
- Build production agentic systems with memory, planning, tools and observability
- Architect for security, privacy, governance and compliance in regulated settings
- Lead an AI transformation: opportunity mapping, ROI cases, change management and vendor selection
- Run an AI product from discovery to launch to iteration on real usage data
- Build a public portfolio, a technical narrative and an interview ready story
- Contribute to open source and ship a capstone to real users
Products and tools you will use
Curriculum
01Foundations and the AI landscape
Month 1- · How transformers, tokens, embeddings and attention actually work
- · Model families, benchmarks and honest capability mapping
- · Python and data handling for AI work, taught from first principles
- · Setting your track: generalist or engineering
02Advanced prompting and context engineering
Month 1- · Chain of thought, tree of thought, self consistency and meta prompting
- · Context window strategy, compression and memory design
- · Structured generation and schema enforcement
- · Prompt versioning and team level prompt operations
03Retrieval, knowledge and multimodal generation
Month 2- · Production retrieval augmented generation with hybrid search and reranking
- · Graph and hierarchical retrieval for large corpora
- · Image, audio, 3D and video generation pipelines
- · Multimodal understanding: documents, screenshots and speech
04Agentic systems
Month 3- · Planner and executor architectures with durable memory
- · Tool calling, computer use and browser automation
- · Multi agent orchestration, delegation and conflict resolution
- · Safety rails, sandboxing and approval workflows
05Fine tuning and model adaptation
Month 4- · When to prompt, when to retrieve, when to fine tune
- · Dataset construction, labelling and synthetic data
- · LoRA and parameter efficient fine tuning on open models
- · Serving, quantisation and inference cost engineering
06Production engineering and MLOps
Month 4- · APIs, queues, streaming and stateful conversations at scale
- · Observability: tracing, evals, drift detection and alerting
- · Security, prompt injection defence, PII handling and governance
- · Deployment, containers, CI and cost control
07AI product and business leadership
Month 5- · Opportunity mapping and ROI modelling for AI initiatives
- · Build versus buy, vendor evaluation and data strategy
- · Change management, training and adoption inside teams
- · Pricing, packaging and go to market for AI products
08Capstone and career
Month 6- · Ship a capstone to real users with real telemetry
- · Architecture write up, technical blog and open source contribution
- · Portfolio, resume and interview preparation for AI roles
- · Demo day in front of practitioners and hiring partners
09Continued support
Months 7 to 12- · Monthly mentor pods and architecture clinics
- · New module drops as the field moves
- · Job and freelance opportunity board
- · Alumni community and referrals
What you will build
- 1.A production retrieval system over a large real corpus with evaluation harness
- 2.A multi agent system that plans, calls tools and reports on its own work
- 3.A fine tuned open model serving a narrow task better and cheaper than a frontier model
- 4.A multimodal application combining text, image, voice and video
- 5.A capstone product shipped to real users with monitoring and a written architecture case study
Questions people ask
Yes, and it is the most common profile. Engineers use the fellowship to move from features to intelligent systems and leave with a portfolio that shows it.
Plan for 12 to 15 hours. This is a fellowship, not a webinar series, and the results track the effort.
The final six months include portfolio review, interview preparation, an opportunity board and alumni referrals. We do not promise placement, we prepare you to earn it.
Yes. The first month is shared. You pick generalist or engineering after it and can move once with mentor agreement.
Ready to start A.I. Mastery?
₹1,24,999 · 6 months intensive plus 6 months continued support · Hybrid fellowship: live cohorts, mentor pods, build sprints and demo days