Student corner

GEN AI DEVELOPER → AGENT BUILDER → JOB-READY ROADMAP

1: Core Foundation (Must-Have Skills)

This phase builds the technical thinking required to survive in AI roles.

Programming Fundamentals

• Python (mandatory)

• Data structures basics

• OOP concepts

• Functional programming mindset

• Writing clean, production-ready code

Math & AI Foundations

• Linear algebra (vectors, matrices – applied level)

• Probability & statistics for ML

• Optimization basics

• Cost functions & loss functions

• Overfitting vs underfitting intuition

Software Engineering Basics

• Git & GitHub

• APIs & REST architecture

• JSON, YAML, environment variables

• Debugging & logging practices

Outcome: Student starts thinking like a developer, not a tool user.

 2: AI Algorithms & Machine Learning Core

This phase separates AI engineers from normal developers.

Machine Learning Algorithms

• Linear & Logistic Regression

• KNN, Naive Bayes

• Decision Trees & Random Forest

• Support Vector Machines

• Clustering (K-Means, Hierarchical)

• Dimensionality Reduction (PCA)

Model Lifecycle

• Data collection & preprocessing

• Feature engineering

• Model training & validation

• Evaluation metrics

• Hyperparameter tuning

Hands-On Projects

• AI model for student performance prediction

• Recommendation system

• Fraud detection mini system

• Text classification engine

Outcome: Student can explain how AI works, not just use libraries.

 3: Deep Learning & NLP Essentials

Now the student enters real Gen AI territory.

Deep Learning

• Neural networks

• Activation functions

• Backpropagation

• CNNs & RNNs (conceptual + applied)

• Transformers architecture (high-level)

Natural Language Processing

• Tokenization & embeddings

• Word2Vec, GloVe

• Attention mechanism

• Transformers & LLM basics

• Prompt engineering fundamentals

Mini Projects

• Sentiment analysis engine

• Chat-based FAQ bot

• Resume analyzer using NLP

Outcome: Student understands how LLMs & Gen AI are built internally.

 4: Generative AI & LLM Engineering

This is where job demand explodes.

Generative AI Core

• LLM architecture understanding

• Text, image, and multimodal Gen AI

• Fine-tuning vs prompting

• In-context learning

• Hallucination control

Prompt Engineering (Advanced)

• System, user & developer prompts

• Chain-of-Thought reasoning

• Prompt optimization patterns

• Structured output prompts

• Tool-calling prompts

LLM Integrations

• OpenAI / open-source LLMs

• Vector databases

• Embeddings & similarity search

• RAG (Retrieval Augmented Generation)

Outcome: Student becomes an LLM Engineer, not a ChatGPT user.

 5: Agentic AI & MaltBot-Style Agent Systems

This is the future-proof skillset.

AI Agent Fundamentals

• What is an AI agent

• Single-agent vs multi-agent systems

• Memory, tools, reasoning & planning

• Agent autonomy & feedback loops

MaltBot-Like Agent Architecture

• Workflow-driven agents

• Trigger-based intelligence

• Tool orchestration

• Decision trees + LLM reasoning

• Event-based execution

Agent Use Cases

• AI sales agent

• Customer support agent

• HR screening agent

• Research agent

• Automation agent

Agent Projects

• Build a MaltBot-style AI assistant

• Multi-agent workflow system

• AI business process automation

• Website AI agent with memory

Outcome: Student can design and build AI agents companies actually want.

 6: Full-Stack AI Product Development

Because companies hire builders, not learners.

Backend for AI Systems

• FastAPI / Flask

• Authentication & security

• API rate limiting

• Database integration

• Cloud deployment basics

Frontend Basics

• AI dashboards

• Chat UI systems

• Admin panels

• Real-time updates

Deployment & Scaling

• Docker basics

• Cloud hosting

• Model serving

• Monitoring & logs

• Cost optimization

Outcome: Student can ship real AI products.

 7: Industry-Level Projects (Portfolio Phase)

This phase is directly aligned with internships & jobs.

Capstone Projects

• AI Agent Platform (MaltBot-like)

• Gen AI SaaS application

• RAG-based enterprise chatbot

• AI automation system for business

• Multi-agent decision system

Project Standards

• Clean GitHub repos

• Documentation & README

• Demo videos

• Use-case explanation

• Architecture diagrams

Outcome: Student portfolio becomes interview-ready.

 8: Internship Readiness & Real-World Exposure

This phase converts skills → experience.

Internship Preparation

• AI project explanation skills

• Code walkthrough practice

• System design interviews

• Debugging live problems

Live Exposure

• Working on real datasets

• Client-style requirements

• Team collaboration

• Agile & sprint methods

Outcome: Student qualifies for AI internships & live projects.

 9: Job Readiness & Placement Strategy

Final transformation into a professional Gen AI Developer.

Career Preparation

• AI-focused resume building

• GitHub & LinkedIn optimization

• Technical interview preparation

• Case-study discussions

• AI system design rounds

Target Roles

• Gen AI Developer

• AI Engineer

• LLM Engineer

• AI Agent Developer

• AI Automation Engineer

Outcome: Student is job-ready with confidence.

Final Transform

By completing this , a student becomes:

✔ Strong in AI algorithms
✔ Expert in Generative AI & LLMs
✔ Skilled in MaltBot-style AI agents
✔ Capable of building production AI systems
✔ Ready for internships, startups, and MNC roles