Why now
AI is already inside how software gets built. The advantage goes to the ones who build it.
This is not a warning. It is the clearest opening developers have had in years.
- 84%
of developers now use AI tools in their work.
Stack Overflow - 71%
business Gen AI adoption, in a single year.
Stanford HAI - 80%
of engineers will need to upskill in AI by 2027.
Gartner
Using AI tools is now the baseline. Building AI systems is the differentiator, and that is what you learn here.
Is this you
Built for working developers ready to build with AI
- JAVA
- .NET
- PHP
- FRONTEND
- BACKEND
- DATA
- MAINFRAME
- QA
- DEVOPS
You do not need to match all of these. Any one of them is enough.
- You have 2 to 12+ years shipping software, in any stack.
- You can code, but you have not built with LLMs or agents yet.
- You are a Java, .Net, PHP, frontend, backend, data or mainframe engineer.
- You are in QA or DevOps and want to move into AI-augmented work.
- You would rather build real AI systems than watch more tutorials.
If even one of these sounds like you, you are in the right place.
No Python? Start here
You can code. You have not built with AI yet. That is exactly the gap we close.
You do not need Python before you start. We take you from the first line to production AI systems, in order, with a mentor beside you.
- 01
Python from the first line
We start at setup and fundamentals, even if you have never written Python. No prerequisite, no catching up alone.
- 02
Structured, step by step
Foundations to Gen AI to agents, sequenced so each module earns the next. Nothing is assumed or skipped.
- 03
The real enterprise stack
You train on the exact tools and workflow companies run in production, not a simplified classroom version.
- 04
Never stuck alone
With a 1:15 mentor ratio, your doubts get cleared in the same session, so you never fall behind.
BlueTick alumni work at companies like these
Across our programs, BlueTick alumni have gone on to build careers at leading MNCs, funded startups and top product teams.
Learn from Industry Experts who are building AI systems for Fortune 500 Companies
A different expert leads each module, so you always learn from someone who has actually built it.
Ramesh Krishnan
20+ YEARS EXPERIENCEAI/ML Senior Manager
Kishore Vutukuri
20+ YEARS EXPERIENCEEnterprise AI Architect
Suresh Pasumarthi
25+ YEARS EXPERIENCEAI Solutions, Product Head
Ravindra Sonawane
15+ YEARS EXPERIENCESenior AI Scientist
Anirudh Ravishankar
7+ YEARS EXPERIENCESenior AI Engineer
Ganesh Jathar
5+ YEARS EXPERIENCEAI Engineer
Tushar Tiwary
4+ YEARS EXPERIENCEAI Engineer
Shaury Srivastav
4+ YEARS EXPERIENCEAI/ML Engineer
The curriculum
The complete AI engineering stack, in 14 weekends
Not a slice of it. From Python basics to production AI systems, sequenced so each weekend earns the next.
Phase 1
Foundation
- Python
- AI Overview
- AI Coding
Phase 2
The Engine
- GenAI Foundation
Phase 3
Knowledge & Autonomy
- RAG
- AI Agents
- MCPs
Phase 4
Production
- AI Evals & LLMOps
Phase 5
Advanced + Capstone
- Advanced Learning
- Capstone
Optional tracks, after the core curriculum
- Machine Learning
- DSA + SQL
The curriculum
15 modules that turn a developer into an AI engineer
Every module is hands-on, taught by industry AI experts, and ends in something real you can show a recruiter.
14 weekends · Saturday & Sunday · ~200 hours
- M11 / 16
AI Overview
- AI vs ML vs Deep Learning vs GenAITHEORY
- Generative AI vs traditional AI, and when each fitsTHEORY
- Enterprise AI maturity curveTHEORY
- The modern AI stack, from models to apps and agentsTHEORY
- Where GenAI, RAG and agents fit in the bigger pictureTHEORY
- Real use cases in banking, retail, healthcare, ITTHEORY
- Build vs buy: how enterprises actually adopt AITHEORY
- The AI engineer role and the career paths it opensTHEORY
- Ethics and responsible AITHEORY
- M22 / 16
Python for AI/ML
- Setting up a clean Python environmentPRACTICAL
- Python fundamentals, even if you have never codedCODE
- Building REST APIs with FastAPICODE
- Building interactive web apps with StreamlitCODE
- OOP concepts with classes and objectsCODE
- Data handling with Pandas and NumPyCODE
- Data visualisation with Matplotlib and SeabornCODE
Tools and frameworks
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- FastAPI
- Streamlit
You’ll walk out with
AI Text Summarization Assistant, a working web app you can demo in interviews.
- M33 / 16
AI Coding
- Coding with Claude, Cursor and GitHub CopilotCODE
- Choosing the right AI coding tool for the taskPRACTICAL
- Context management and instruction filesPRACTICAL
- Prompting patterns for code generationPRACTICAL
- Spec-driven developmentPRACTICAL
- Reviewing and refactoring AI-generated codeCODE
- Writing and running tests with AICODE
- Generating documentation from your codebasePRACTICAL
- Debugging with AICODE
Tools and frameworks
- Cursor
- Claude
- GitHub Copilot
You’ll walk out with
A working application built end to end using Claude.
- M44 / 16
GenAI & LLM Foundations
- How LLMs work, Transformers explained simplyTHEORY
- Reasoning and Chain-of-Thought modelsTHEORY
- Prompt engineering for the output you needPRACTICAL
- Prompt evaluation and optimisationPRACTICAL
- Working with OpenAI, Gemini and Claude APIsCODE
- Diffusion models for image generationPRACTICAL
Tools and frameworks
- LangChain
- OpenAI API
- Gemini API
- Claude API
You’ll walk out with
A working chatbot powered by an LLM API, the foundation you extend in every later module.
- M55 / 16
RAG (Retrieval Augmentation)
- RAG architecture end to endTHEORY
- Vector embeddings, chunking and indexingCODE
- Retrieval and rerankingCODE
- Vector databases: Chroma, Pinecone, WeaviateCODE
- LlamaIndex for production RAGCODE
- Graph RAG and evaluation with RAGASPROJECT
Tools and frameworks
- LlamaIndex
- ChromaDB
- Pinecone
- Weaviate
- RAGAS
- Graph RAG
You’ll walk out with
Enterprise Document Q&A System, a production retrieval system that answers questions over your own documents.
- M66 / 16
Agentic AI
- Agents: LLM + tools + memory + planningTHEORY
- Agent workflows and orchestrationPRACTICAL
- Building agents with LangGraphCODE
- SQL Agent that queries databases in plain EnglishCODE
- Context engineering: why agents failPRACTICAL
- Agentic RAG and agent memoryCODE
- Multi-Agent Systems with CrewAI and MCPPROJECT
Tools and frameworks
- LangGraph
- CrewAI
- MCP
- LangChain
You’ll walk out with
SQL Agent plus Multi-Agent Enterprise Data Assistant, your portfolio centrepiece.
- M77 / 16
Capstone Project
- Scope a real corporate use case into an AI solution designTHEORY
- Build a production RAG pipeline over enterprise documents with LangChainCODE
- Design a multi-step Agentic AI workflow in LangGraphCODE
- Wire agents to tools, memory and retrievalCODE
- Orchestrate the agent and RAG systems into one Python applicationCODE
- Test, debug and harden the system for real-world inputsPRACTICAL
- Package and present it as an interview-ready portfolio projectPROJECT
Tools and frameworks
- LangChain
- LangGraph
- Python
You’ll walk out with
An Agentic AI + RAG system built on a real corporate use case, your portfolio centrepiece.
- M88 / 16
AI Evals & LLMOps
- AI evaluation: knowing if your AI actually worksTHEORY
- LLM-as-Judge evaluation techniquesPRACTICAL
- Tracing and observability in productionPRACTICAL
- Guardrails against hallucinations and unsafe outputCODE
- AI task orchestration with n8nPRACTICAL
- Cutting LLM cost without losing qualityPRACTICAL
Tools and frameworks
- Phoenix
- Opik
- LangSmith
- n8n
- Zapier
- Make
You’ll walk out with
Customer Support Agent with evaluation and observability, a production-ready system recruiters recognise.
- M99 / 16
Multi-modal AI using CrewAI
- Multi-modal LLMs: reasoning over text, images and audio togetherTHEORY
- Designing a crew of role-based agents with CrewAICODE
- Coordinating tasks, roles and delegation across a multi-agent crewPRACTICAL
- Feeding agents multi-modal inputs and toolsCODE
- Building a pipeline that reads images and documentsCODE
- Sharing memory and context between agents in a crewCODE
- A working multi-modal, multi-agent application with CrewAIPROJECT
Tools and frameworks
- CrewAI
- OpenAI API
- Gemini API
You’ll walk out with
A multi-modal AI crew that reasons over text, images and audio together.
- M1010 / 16
Workflow Automation with n8n
- Workflow automation fundamentals and where it pays offTHEORY
- Building AI workflows visually in n8nPRACTICAL
- Triggering workflows from events, webhooks and schedulesCODE
- Connecting LLMs and agents inside an n8n workflowCODE
- Integrating business tools: email, Sheets, CRMs and APIsCODE
- Adding branching, error handling and retries to automationsPRACTICAL
- An end-to-end automated AI workflow built in n8nPROJECT
Tools and frameworks
- n8n
- Zapier
- Make
You’ll walk out with
An end-to-end AI automation that triggers, orchestrates and acts on its own in n8n.
- M1111 / 16
MCPs & Google ADKs
- What MCP is and why it standardises tool accessTHEORY
- Connecting AI to any data source with MCPCODE
- Building and running your own MCP serversCODE
- Using Google ADKs to build production agentsCODE
- Exposing tools, files and APIs to your agents safelyPRACTICAL
- Composing multiple MCP servers into one agent systemCODE
- A data assistant that talks to your own sources via MCPPROJECT
Tools and frameworks
- MCP
- Google ADK
You’ll walk out with
A local file and data assistant that reaches your own sources through MCP.
- M1212 / 16
Design Thinking with AI
- Design-thinking fundamentals for AI productsTHEORY
- Empathising with users and framing the real problemTHEORY
- Ideating AI solutions that fit the use casePRACTICAL
- Mapping a problem to the right pattern: RAG, agents or automationPRACTICAL
- Rapid prototyping of AI features with StreamlitCODE
- Testing and validating ideas with real usersPRACTICAL
- A validated AI product concept, prototyped end to endPROJECT
Tools and frameworks
- Streamlit
You’ll walk out with
A validated AI product concept taken from problem framing to a working prototype.
- M1313 / 16
Voice Agents
- How voice agents work: speech-to-text, LLM, text-to-speechTHEORY
- Building natural voice interfaces with ElevenLabsCODE
- Transcribing and understanding user speechCODE
- Driving a voice agent with an LLM and toolsCODE
- Adding memory and turn-taking to a conversationPRACTICAL
- Handling latency, interruptions and real-time flowPRACTICAL
- A working voice agent that speaks and listensPROJECT
Tools and frameworks
- ElevenLabs
- LangGraph
You’ll walk out with
A production-style voice agent that listens, reasons and replies in natural voice.
- M1414 / 16
Advanced Learning
- Agent-to-Agent (A2A) protocols for multi-system collaborationTHEORY
- Designing agents that discover, message and delegate to each otherCODE
- When to fine-tune vs prompt vs retrieveTHEORY
- LLM fine-tuning with LoRA and QLoRACODE
- Parameter-efficient fine-tuning with PEFTCODE
- LLM cost optimisation: cutting spend without losing qualityPRACTICAL
- Model selection, caching and token strategies for productionPRACTICAL
Tools and frameworks
- LoRA
- QLoRA
- PEFT
You’ll walk out with
A fine-tuned, cost-optimised model plus a multi-agent setup that collaborates over A2A protocols.
- M1515 / 16Optional · self-paced
Machine Learning
- Regression, Decision Trees, Random Forest, XGBoostCODE
- Overfitting, bias and variance, explainabilityTHEORY
- Unsupervised: K-Means, DBSCAN, PCACODE
- Computer Vision and Object Detection (YOLO)PROJECT
- Model evaluation and cross-validation metricsPRACTICAL
- Forecasting: time series, ARIMA, AutoGluonCODE
- Deep Learning: Neural Nets, CNN, RNNCODE
- MLOps with MLflow and model monitoringPRACTICAL
Tools and frameworks
- scikit-learn
- XGBoost
- LightGBM
- AutoGluon
- TensorFlow
- YOLO
- MLflow
- M1616 / 16
Capstone Project & Interview Prep
- Architecting a multi-modal AI system end to endPRACTICAL
- Connecting tools and data with MCPs and ADKsCODE
- Adding a voice-agent interface to your systemCODE
- Automating the workflow with n8nCODE
- Integrating retrieval, agents and automation into one productPROJECT
- Building your portfolio and project walkthroughPROJECT
- Mock interviews and AI-engineer interview prepPRACTICAL
Tools and frameworks
- MCP
- Google ADK
- ElevenLabs
- n8n
You’ll walk out with
A multi-modal AI system spanning MCPs, ADKs, voice and n8n automation, plus an interview-ready portfolio.
Phase 5 · Get hired
You finish with proof, not just a certificate
Four days, 16 hours, to turn skills into offers.
Capstone Project
Apply every module in one production-grade project, your portfolio centrepiece for interviews.
AI-Readiness Assessment
A structured assessment that mirrors real enterprise hiring rounds, so you know where you stand before your first interview.
Mock Interviews
- Technical interviews with industry practitioners
- Real-company 1:1 simulations led by AI experts
- Detailed feedback after every round
You walk into your first interview having already done five.
You'll build with 50+ of the AI tools the industry uses today
Every tool is taught hands-on, never just shown on a slide. Here are a few you'll get comfortable with. Your track covers the full stack.
+ 40 more, taught hands-on across your track
The payoff
AI skills are among the best-paid, fastest-growing skills in tech
Learn to build with AI now and you build your career on one of the strongest foundations in the market.
- ~56%average pay premium for professionals with AI skills.PwC
- 62%higher offers for people with Gen AI skills.Scaler
- ~40%a year growth in AI skill demand.NASSCOM
- ~16%of IT professionals are AI-skilled today.MeitY
The developers who build with AI now are the ones who lead the teams later.
Figures are indicative, drawn from public industry reports.
The BlueTick advantage
Why this is not another AI course
Enterprise-grade AI projects
You build real production systems, not just calling the OpenAI API once and calling it a project.
100% hands-on learning
You learn by building, every session. No boring theory, no watch-alone lectures.
Modern AI stacks coverage
50+ current tools across the full stack, the exact ones companies are hiring for now.
Industry AI expert mentors
You learn from experts who build AI systems for Fortune 500 companies, a different one for each part of the course.
Complete placement support, for as long as you need it
Your learning doesn't stop at the last class. Our career team works with you personally, from portfolio to referrals.
Portfolio review
We help you shape your enterprise-grade AI projects into a portfolio recruiters want to see.
Resume & LinkedIn
We polish your resume and LinkedIn so your new AI skills stand out to the right people.
Mock interviews
Practice real interviews with our industry AI experts, so you walk in confident and ready.
Job assistance & referrals
We connect you to real openings and refer you through our network.
We train you for exactly what interviews ask, so you walk in ready to crack them.
What Gen AI and Agentic AI skills can do for your salary
Same experience. New skill. Here's the jump these roles typically pay in India — by career stage.
- up to 2×salary increases 2 timesFresher0-2 yrs
- up to 1.9×salary increases 1.9 timesMid-level3-7 yrs
- up to 1.6×salary increases 1.6 timesSenior8+ yrs
All figures in LPA (₹ lakh per year) · Source: Glassdoor, NASSCOM 2026, BusinessToday (Jan 2026).
Indicative market ranges — actual pay depends on role, skills and performance.
Learn your way
Weekends, in person or live online
Weekends, Saturday and Sunday, 10 AM to 2 PM. Built to fit around a full-time job.
Offline at our campus
Learn in person at our Indiranagar campus, a one-minute walk from Indiranagar Metro, with free parking.
Or the same class, live online
Cannot travel? Join the very same live session online, with the same mentor and the same batch. Never a recording instead.
2nd Floor, 545 CMH Road, Indiranagar, Bengaluru 560038
Same expert mentors, same live class, whichever you choose.
Upcoming batches
Seats fill fast. Book a free demo and reserve your place in the next batch.
Weekend batches: Saturday & Sunday, 10 AM to 2 PM. Attend offline in Indiranagar or live online.
Our July batch is already full. Reserve your seat for August.
QA & DevOps, you fit here too
Your QA or DevOps experience is a head start into AI
You already understand systems, pipelines and quality. We add the AI layer that the best teams now build on top.
If you are in QA
- Move from manual and scripted testing to agentic test automation.
- Build AI agents that generate, run and triage test cases.
- Add LLM evaluation and guardrails to your quality toolkit.
If you are in DevOps
- Bring AI into your pipelines: AI-augmented CI/CD and automation.
- Run LLMOps: tracing, observability and cost control in production.
- Orchestrate agentic automation across your infrastructure.
You are not switching careers. You are adding the layer that makes your career future-proof.
Your questions, answered
I'm a working developer but this looks basic. Will it be too easy for me?
It starts from Python fundamentals so nobody is left behind, but it moves fast into the parts that matter for you: RAG, AI agents, multi-agent systems, LLMOps and fine-tuning. Even senior engineers tell us the agentic and production modules are new ground. You spend most of the program building real, enterprise-grade AI systems, not revisiting basics.
I have never written Python. Can I still do this?
Yes. The first module teaches Python from setup and fundamentals, built for people who have never touched it, right up to building APIs and apps. You already think like an engineer, so the syntax comes quickly, and with a 1:15 mentor ratio your doubts get cleared in the same session.
What will I actually build?
Real systems you can show a recruiter: an LLM-powered chatbot, a production RAG document Q&A system, a SQL agent, a multi-agent enterprise data assistant, a customer-support agent with evaluation and observability, and a capstone that ties it all together. You leave with a portfolio, not just a certificate.
Do the mentors actually build AI, or just teach it?
They build it for a living. You learn from 9 industry AI experts who build AI systems for Fortune 500 companies, with backgrounds at EY, IBM, PwC, Infosys and Microsoft, and degrees from institutions like IIT Kanpur. A different expert leads each module.
I have a full-time job. Can I manage this?
Yes, it is built for working developers. Classes run on weekends, Saturday and Sunday from 10 AM to 2 PM, over 14 weekends. Attend offline at our Indiranagar campus or join the same live session online. You keep your job and build your AI skills in parallel.
I'm in QA or DevOps, not core development. Does this fit me?
Absolutely. There is a dedicated focus for you: QA moves into agentic test automation, and DevOps into AI-augmented pipelines, LLMOps and agentic automation. Your systems and quality experience is a real head start into AI work.
Is this real engineering or just prompt writing?
Real engineering. You build production AI systems: RAG pipelines, multi-agent architectures, evaluation and observability, MCP integrations and fine-tuning with LoRA and QLoRA. Prompting is one small part of a much deeper, hands-on build.
Will this actually move my career forward?
Gen AI and Agentic AI are among the fastest-growing, best-paying skills in tech right now. You finish with the production stack companies are hiring for, a portfolio of real projects, and complete career support: portfolio reviews, resume and LinkedIn help, mock interviews and referrals, for as long as you need it.
Six months from now, you could be the one building the AI
Book a free demo, sit in on a live class, and take the first step from using AI to building it.
The developers who build with AI now are the ones who lead later. Start today.