CS Graduate
Objective
Seeking a challenging software role in a reputed organisation where I can use my skills and grow.
Education
BE / BTech CS, 2025
Skills
- Java
- C++
- DBMS
- HTML / CSS
- Communication
Projects
Academic only
Certifications
None
For CS & IT graduate freshers · 0 to 1 year
Land Your First AI Job as a Builder
Your degree is your strength. AI skills get you hired.
5 months · Weekdays or weekends · Offline or online
- Foundations to AI agents
- A portfolio that gets shortlisted
- Interview prep + placement support
- Small batches · 1:15 mentor ratio
The fresher market, honestly
The fresher market got harder. That is exactly your opening.
The hard truth first
44%drop in fresher job openings, year on year.Xpheno 2025
20-25%of entry-level roles are being automated away.EY 2025
~20%fall in young-developer hiring since 2022.Stanford 2025
But every number that scares the crowd is the same number that clears your path.
Now the other side
1M+AI professionals India needs and cannot find.MeitY
~16%of the workforce is actually AI-skilled today.MeitY
<3%of 1.5M graduates have real AI skills.NASSCOM
~40%a year demand growth, while supply crawls.NASSCOM
Learn to build with AI now and you stop competing with the crowd. You become the differentiator.
Figures are indicative, drawn from public industry reports.
Your degree counts
Your degree is not wasted. It is your head start.
WHAT COLLEGE GAVE YOU
WHAT WE ADD ON TOP
A real grasp of how software and systems work.
The current Gen AI and Agentic AI stack companies hire for.
Programming logic and problem solving you can build on.
A portfolio of real projects, not just assignments.
The habit of learning hard things and finishing them.
Interview practice built around real hiring rounds.
A foundation many career switchers are still catching up on.
The industry workflow that turns knowledge into a job.
We do not replace your degree. We bridge it to the industry stack.
Is this you
This track is built for you
You do not need all five. Any one of these is enough.
You are a CS or IT graduate, or you graduate this year.
1 / 5You can code, or you are close and ready to build.
2 / 5You have 0 to 1 year of experience and want a strong first AI job.
3 / 5You have an AI or ML degree but no production experience yet.
4 / 5You would rather build real projects than collect certificates.
5 / 5
If even one of these sounds like you, you are in the right place.
Built for freshers
You do not need prior AI experience to start.
Foundations first, then agents
We start from the basics and build up in order, from core concepts to Gen AI to real AI agents. Nothing is assumed.
A mentor for every 15 learners
With a 1:15 mentor ratio, your questions get answered the same day and never pile up.
The real enterprise stack
You train on the exact tools and workflow companies use in production, not a simplified classroom version.
Am I good enough for this? Yes. The whole course is built so that you are.
Where the hiring moved
Data science is not the only door anymore.
FRESHERS IN 2026
The old default
- Statistics-first classical machine learning.
- Taught in almost every course, so the field is crowded.
- Fewer fresher openings than a few years ago.
- Still useful, but no longer where the new demand is.
Where the demand is now
- Gen AI and Agentic AI: LLMs, RAG and AI agents.
- 20 to 40 percent higher offers for Gen AI skills.
- Few academies in India teach this stack deeply.
- The exact skills 2026 hiring is asking freshers for.
This is the track you are on.
Classical machine learning is not thrown away. It stays here as an optional track you can add.
The demand did not disappear. It moved to Gen AI and Agentic AI.
Salary figures are indicative, drawn from public industry reports.
The curriculum
Your 5-month roadmap
From Python basics to production AI systems, sequenced so each phase earns the next. Five months, weekday or weekend.
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
Included in the program
- DSA + SQL for Interviews
Optional track, after the core curriculum
- Machine Learning
The curriculum
18 modules that turn a fresher into an AI engineer
Every module is hands-on, taught by industry AI experts, and ends in something real you can show a recruiter.
5 Months · Weekdays / Weekends · ~200 Hours
- M11 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
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 / 18
SQL
- How SQL thinks: execution order and a 6-step query frameworkTHEORY
- SELECT, filtering and sorting to slice any datasetCODE
- JOINs and aggregations across multiple tablesCODE
- Window functions for ranking, running totals and comparisonsCODE
- CTEs and subqueries to break down complex questionsCODE
- Interview patterns: Top-N, dedup and running totalsPRACTICAL
- A timed mock SQL round on real interview questionsPROJECT
Tools and frameworks
- PostgreSQL
- SQLAlchemy
- LeetCode patterns
You’ll walk out with
A solved bank of interview SQL patterns plus a timed mock round you can repeat before every interview.
- M1616 / 18
DSA for Interview Prep
- Arrays and two pointers: prefix sum, Kadane's, sliding windowCODE
- Binary search on answers and index spacesCODE
- Stacks, queues and linked lists, including LRU cacheCODE
- Trees and recursion with DFS and BFS traversalsCODE
- Backtracking for combinations, permutations and subsetsCODE
- Graphs, heaps and dynamic programming fundamentalsCODE
- Blind 75 walkthrough and an online-assessment strategyPROJECT
Tools and frameworks
- Python
- Blind 75
- LeetCode patterns
You’ll walk out with
A worked Blind 75 pattern set plus an OA and mock-round strategy that turns coding rounds into a formality.
- M1717 / 18Optional · 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
- M1818 / 18
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 your new skills into offers.
4 days · 16 hours
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
Real interview practice with industry AI experts, so you walk into your first one already warmed up.
- Technical interviews with industry practitioners
- Real-company 1:1 simulations led by AI experts
- Detailed feedback after every round
- Walk into your first interview having done five
As a fresher, your first interview is the hardest. You walk in having already done five.
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.
MNCs & MAANG
Funded Startups
Hiring Partners
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
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
Before and after
In about five months, your resume looks completely different
Same degree. A portfolio of real AI systems heavier.
Job-ready AI Engineer
Education
BE / BTech CS + Gen AI & Agentic AI program, BlueTick Academy
Projects
- Production RAG document Q&A system on GitHub, with retrieval, reranking and evaluation
- SQL Agent that queries databases in plain English
- Multi-agent enterprise data assistant built with LangGraph and CrewAI
- Customer-support agent with evaluation and observability (LLMOps)
Skills
- Python
- LangChain
- LangGraph
- RAG
- AI Agents
- MCP
- Claude
- OpenAI
Extras
- ATS-optimised resume
- Mock technical interviews done
- SQL + DSA rounds practised
This is the difference five months makes. Not a longer skills list, a portfolio's worth of credibility.
The interview
How most freshers walk into an AI engineer interview, and how BlueTick students walk in
How others attend interviews
- "I've watched a few tutorials on LangChain and Gen AI."
- Resume lists a certificate, but no projects.
- Can name the tools, but freezes when asked to build an agent.
- Asked "show me something you've built" and has nothing to open.
- Talks about "wanting to learn". Sounds like a beginner.
How BlueTick students attend interviews
- "I built a multi-agent assistant that answers questions over enterprise data."
- Resume opens with a GitHub portfolio a recruiter can click into.
- Opens a laptop and walks through a live RAG system.
- Asked to write a query or wire up an agent, does it, and explains the logic.
- Talks about what they've already shipped. Sounds like a junior AI engineer.
The difference isn't the certificate. It's the work you can open on your laptop.
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.
Enterprise-grade AI projects100% hands-on learning50+ applied AI toolsNo boring theory
Learn your way
Weekday or weekend. In person or live online.
Weekdays Monday to Friday, 9 to 11 AM. Or weekends Saturday and Sunday, 10 AM to 2 PM. Pick what fits your final year or your job hunt.
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.
JUL 18Sold out
NEXT BATCHAUG 2Filling fast
AUG 17Available
Weekday batches: Monday to Friday, 9 AM to 11 AM. 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.
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.
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 Gen AI and agent 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.
Questions, answered
Your questions, answered
I'm a fresher. Will this course actually get me a job?
We do not promise a guaranteed placement, and you should be careful of anyone who does. What we promise is that you become genuinely job-ready. You build a real portfolio of Gen AI and agent projects, practise real interviews, and get complete career support: portfolio reviews, resume and LinkedIn help, mock interviews and referrals, for as long as you need it. Gen AI and Agentic AI roles are in high demand, so the skills you build here are exactly what companies want.
Is the fresher AI job market actually real, or is it just hype?
It is real, and the numbers show it. India needs over a million AI professionals and cannot find them, only about 16% of the workforce is AI-skilled today, and demand is growing around 40% a year while supply crawls. Fresher openings in general have tightened, but AI-skilled freshers are scarce. Learn to build with AI and you become the person companies are actually looking for.
Is this too advanced for a fresher?
No. The course starts from Python fundamentals, even if you have never coded, and builds up in order to Gen AI and AI agents. Nothing is assumed, and with a 1:15 mentor ratio your doubts get cleared the same day. It is built so that a motivated fresher can go from foundations to building real AI systems.
Why Gen AI over data science or classical ML?
The demand has shifted. Classical data science and ML are taught almost everywhere, so the field is crowded and fresher openings are fewer. The new hiring is in Gen AI and Agentic AI, with 20 to 40 percent higher offers for Gen AI skills, and few academies in India teach this stack deeply. Classical ML is not thrown away, it stays here as an optional track you can add.
I have a CS or AI/ML degree. Do I still need this?
Your degree is a real head start, and we build on it rather than repeat it. What most degrees do not give you is production experience with the current Gen AI and Agentic AI stack: RAG, AI agents, LLMOps and a portfolio of real, deployable projects. That is exactly the gap this course closes, so you walk into interviews with proof, not just a transcript.
What portfolio will I 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 recruiters can open, not just a certificate.
How does placement support work?
Our career team works with you personally after the classes: shaping your projects into a strong portfolio, polishing your resume and LinkedIn, running mock interviews with our AI experts, and referring you through our network, for as long as you need it. We commit to the support, not to a guaranteed outcome, and we train you for exactly what interviews ask.
Can I fit this alongside my final year, internship or job hunt?
Yes. You choose weekday batches, Monday to Friday from 9 to 11 AM, or weekend batches, Saturday and Sunday from 10 AM to 2 PM, whichever fits your schedule. Attend offline at our Indiranagar campus or join the same live session online. It is built to run in parallel with your final year or your job search.
The fresher who builds with AI is the one who gets hired
Book a free demo, sit in on a live class, and take the first step towards your first AI job.
Your degree got you here. AI skills get you hired. Start today.