Let's Be Honest About Your Career Right Now
You've mastered pandas.
You can build a random forest in your sleep.
Your SQL queries are poetry.
And yet, here you are—stuck explaining to your PM why the conversion rate dipped 0.3% last Tuesday.
You didn't get into data science to be a glorified Excel replacement.
While you're building dashboards, the world has moved to AI agents. ChatGPT has more decision-making power than you do. GitHub Copilot writes better code than your junior developers. And somewhere, an AI agent just negotiated a better deal than your sales team.
The painful truth?Your skills have the shelf life of a Bangalore startup's office lease.
But here's the thing: You're actually perfectly positioned to make the leap. You understand data. You know ML. You can code. You just need to learn how to build systems that act instead of systems that report.
While you're building dashboards, the world has moved to AI agents. ChatGPT has more decision-making power than you do. GitHub Copilot writes better code than your junior developers. And somewhere, an AI agent just negotiated a better deal than your sales team.
The painful truth?Your skills have the shelf life of a Bangalore startup's office lease.
But here's the thing: You're actually perfectly positioned to make the leap. You understand data. You know ML. You can code. You just need to learn how to build systems that act instead of systems that report.
What If You Could Build AI That Actually Does Things?
Imagine walking into Monday standup and saying:
"I built an agent that handles 80% of our customer queries. My system monitors competitor pricing and adjusts ours in real-time. The recruitment agent I built screened 500 candidates overnight"
Not a model. Not a dashboard. An actual autonomous system that takes actions.
This is the difference between being a data scientist and being an AI engineer. And this gap? It's worth ₹15-30 lakhs extra in salary.
Program Structure
Three weekends | Zero fluff | Just you and the future of AI
The AI Power Up Program - Built specifically for data scientists who are tired of being order-takers and ready to become builders.
Weekend 1: Your First Autonomous Agent
What You'll Build: An Intelligent Research Assistant with reflection and memory
PROJECT 1: Domain-Specific Data Retrieval Agent
PROJECT 2: Intelligent Research Assistant (Production Version)
Foundations & First Agent
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Understand AI agents vs chatbots: Perception, Reasoning, Memory, Action
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Set up Python environment (Python 3.11+, OpenAI/Claude API, cost tracking)
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Build your first ReAct agent from scratch with domain-specific tools
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Learn Agent Design Patterns: ReAct, Reflection, Planning, Tool-use (with code)
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Git & GitHub essentials for AI projects (.gitignore for API keys, version control)
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Streamlit basics: Create localhost UI for your agent
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Output: Working domain-specific agent with UI pushed to GitHub
LangGraph & Advanced Patterns
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Introduction to LangChain & LangGraph (stateful flows, controllable agents)
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Rebuild agent in LangGraph: Define state schema, create nodes, conditional edges
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Implement memory architectures: short-term, long-term, conversation persistence
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Add multiple tools: Web search, APIs, custom data sources, knowledge bases
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Reflection Pattern implementation: Build self-correction loop for accuracy
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Basic evaluation methods: Define metrics (accuracy, latency, cost), unit testing
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Enhanced Streamlit UI: Streaming responses, show reasoning steps, cost tracker
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Output: LangGraph agent with 4+ tools, reflection, memory, professional UI
Weekend 2: The Multi-Agent Collaboration System
What You'll Build: 3-Agent Specialized Team for Complex Task Automation
Multi-Agent Foundations
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Multi-agent concepts: When to use, agent specialization, communication patterns
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Build simple 2-agent system: Agent 1 gathers data → Agent 2 analyzes and recommends
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Agentic Design Patterns deep-dive: Planning, Reflection, Tool-use, Multi-agent delegation
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LangGraph for multi-agent orchestration: Shared state, conditional routing, error handling
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CrewAI basics: Role-based agents, Tasks, Tools, Sequential vs Hierarchical processes
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Rebuild 2-agent system in CrewAI and compare with LangGraph
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Output: Working 2-agent collaboration in both frameworks
Advanced Multi-Agent & Planning
Choose your domain:
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FinTech: Analyst → Risk Manager → Trader
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Healthcare: Intake → Diagnosis → Treatment Planner
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E-commerce: Researcher → Marketer → Support Agent
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HR Tech: Screener → Interviewer → Evaluator
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Advanced CrewAI: Build domain-specific team with 3 specialized agents
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Define 3 specialized agent roles with task dependencies and hierarchical coordination
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Planning Pattern implementation: Plan-and-Execute, dynamic replanning, task prioritization
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Integrate LangGraph + CrewAI patterns for complex workflows
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Add reflection to each agent for quality assurance
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Memory management across agents with tool integration (APIs, databases)
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Agent evaluation & testing: Multi-agent metrics, inter-agent communication, end-to-end scenarios
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Performance benchmarking: Speed, cost, accuracy (A/B test single vs multi-agent)
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Output: Production-quality 3-agent system with comprehensive tests
PROJECT 3: Researcher + Analyst Duo
PROJECT 4: 3-Agent Domain-Specific System
Weekend 3: Production Deployment & Polish
What You'll Build: Live AI Agent Platform with monitoring and business case
PROJECT 5: Optimize and Deploy Multi-Agent System to Production
PROJECT 6: Complete AI Agent Platform Package
Optimization & Deployment
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Performance optimization: Prompt engineering, model selection (GPT-4 vs 3.5 vs Claude), caching
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Error analysis & handling: Retry logic, fallback strategies, graceful degradation, alerting
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Human-in-the-loop: Approval workflows for critical actions, confidence thresholds, manual override
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Monitoring setup: LangSmith (trace decisions)Deployment options: Streamlit Cloud (free tier), Modal.com (serverless), Docker + Railway
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Deploy to Streamlit Cloud: GitHub integration, configure secrets, test live deployment
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Output: Live, accessible Multi-Agent System with monitoring dashboard
Advanced Intelligence & Capstone
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Advanced features: GPT-4 Vision (image analysis), Whisper (voice input), document processing
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Long-term memory: Vector databases (ChromaDB, Pinecone), episodic memory, user preferences
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Professional UI development: Advanced Streamlit components, custom CSS, authentication
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Chat history persistence, export results (PDF, CSV, JSON), mobile-responsive design
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Complete documentation package: Architecture diagram, API docs, user guide, troubleshooting
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Demo video recording: 5-minute professional presentation (problem, solution, demo, impact)
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Business case & ROI calculation: Problem quantification, cost analysis, payback period, savings
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Executive presentation deck with adoption strategy
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Live presentations & feedback: Demo system, peer review, certificate of completion
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Output: Production-ready AI Agent Platform with documentation, demo video, business case
What You'll Actually Build
Not theoretical exercises.
Not Kaggle competitions.
Real systems that create business value.
Your Choice of Industry Application
FinTech Track
Analyst → Risk Manager → Trader
Healthcare Track
Intake → Diagnosis → Treatment Planner
E-Commerce Track
Researcher → Marketer → Support Agent
HR Tech Track
Screener → Interviewer → Evaluator
Weekend 1: Your First Autonomous Agent
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Taught AI/ML cohorts for IIT programs (yes, those IITs)
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Built production agent systems serving 100K+ users
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Worked with startups and enterprises across FinTech, Healthcare, and E-commerce
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Shipped code in Python, deployed on all major clouds, debugged things you haven't even broken yet
Dr Rajita Somina, PhD BITS Pilani
Your Instructor: Someone Who's Actually Done This
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Taught AI/ML cohorts for IIT programs (yes, those IITs)
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Built production agent systems serving 100K+ users
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Worked with startups and enterprises across FinTech, Healthcare, and E-commerce
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Shipped code in Python, deployed on all major clouds, debugged things you haven't even broken yet
Why She's Different:
Most instructors teach theory. Rajita teaches you what actually works when your agent crashes at 3 AM and your CEO is asking questions.
She's been the engineer deploying models that actually make money. She knows exactly where you are and exactly where you need to go.
Dr Rajita Somina, PhD BITS Pilani
Why This Isn't Another Online Course
The Snapskill Difference
We don't teach 47 frameworks. We teach the 3-4 that actually matter, deeply enough that you can build real systems.
01 Depth Over Breadth
Every concept is tied to a real business problem. No toy datasets. No academic abstractions.
02 Industry Applications First
Six sessions. One coherent project. By the end, you have a portfolio piece, not a certificate.
03 Build While You Learn
Rajita has shipped production AI systems using cutting edge tech. She's taught IIT cohorts and built agents serving 100K+ users. You learn what actually works, not what sounds good in papers.
04 Taught by Practitioners, Not Theorists
Maximum 25 students. You're not a number in a Zoom call. You get actual attention.
05 Small Cohorts, High Touch
Questions after the program? We're here. Need help debugging? We're here. Want to discuss your next project? We're still here.
06 Post-Program Support
2026
₹20L
2028
₹35L
2025
₹8L
From Data Analyst to AI Engineer: The 3-Year Projection:
2025 - Today
₹8L Average Mid-Level Data Scientist
Building dashboards, running queries, making charts
2026 - After AI Power Up
₹20L AI/ML Engineer
Building autonomous systems, shipping production agents
2028 - With Experience
₹35L Senior AI Engineer
Leading AI initiatives, architecting multi-agent systems
Your Salary Growth Trajectory
Your Salary Growth Trajectory
The Investment Math
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Program Cost: ₹29,999
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ROI: 3X-4X over three years
And that's just money. The real return? Building things that matter instead of reporting things that don't.
You're brand new to programming
You haven't worked with any ML models before
You want a certificate more than skills
You're looking for a "get rich quick" scheme
You can't dedicate focused weekend time
You want a Job Guarantee
Have 1-5 years in data science or ML engineering
Can write Python without Stack Overflow for basic tasks
Understand ML fundamentals (you know what a gradient is)
Are frustrated with pure analysis work
Want to build autonomous systems, not just models
Can commit 10-12 hours per weekend
Who This Is For (And Who It Isn't)
Straight Talk: This is intense. Three weekends of focused learning and building. If you want something casual, there are YouTube tutorials for that.
Frequently Asked Questions
Q: I'm good with ML but haven't used LLMs much. Will I struggle?
A: If you can build a scikit-learn model, you can build an agent. We start from LLM basics and move fast.
Q: Can I complete this while working full-time?
A: Yes, that's exactly who this is for. Weekends only, with optional integration time.
Q: What if I miss a session?
A: All sessions are recorded. But real talk—miss too many and you'll struggle. This is intensive by design.
Q: Will this help me switch companies or get promoted?
A: Here's what past students did: 3 switched to AI Engineering roles, 2 got promoted internally, 1 started an AI consulting practice. Your mileage may vary.
Q: Do I need to know specific frameworks like LangChain?
A: Nope. We teach everything from scratch. Just bring Python skills and willingness to learn.
Q: What's the refund policy?
A: Full refund if you attend Weekend 1 and feel this isn't for you. After that, no refunds (because you'll already be halfway to building something awesome).