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EdZul · Advancing with AI

Applied AI Engineering Bootcamp

From College Concepts to Production AI Applications

Students today know ChatGPT and AI tools. Very few understand how modern AI products are actually designed, built, integrated and deployed in industry. This workshop closes that gap.

Invite Us to Your CollegeView Workshop CurriculumMeet the Mentors

This is not a basic AI awareness seminar.

Students already know the chat box. We teach the engineering underneath it.

  • how an LLM actually becomes part of a product
  • how AI talks to databases and APIs
  • how RAG works
  • how MCP servers work
  • how AI agents use tools
  • how modern web applications are architected
  • how real engineering teams build AI-powered products
  • how applications are deployed into production
  • what engineering expectations look like inside product companies

Lead mentor

Learn from people who ship products

The emphasis is not the logo wall. It is the engineering judgement students only usually see after they join a product team.

S. Md. Yaseen

S. Md. Yaseen

Founder, EdZul | Senior Software Engineer & AI Product Builder

Nearly a decade of software engineering and product development. Career started in 2017.

S. Md. Yaseen is a software engineer and product builder with nearly a decade of experience across startups, fintech, consumer platforms, enterprise software and AI products.

His career has included engineering environments such as Microsoft, Goldman Sachs, BYJU’S, CRED and Ola/Krutrim.

His experience spans modern web engineering, scalable application architecture, AI product development, RAG, LLM integrations, tool calling, MCP, APIs, real-time systems and production software development.

Through EdZul workshops, his objective is to expose students to the engineering knowledge that usually becomes clear only after entering the software industry.

“Students should not wait until their first job to understand how real products are built.”

Former employers

  • Microsoft
  • Goldman Sachs
  • Ola / Krutrim
  • BYJU'S
  • CRED
AI EngineeringRAGMCPNext.jsReactNode.jsSystem DesignAI Product Architecture

The problem

The gap between college and industry

College typically teaches

  • Programming fundamentals
  • Data structures
  • DBMS
  • Operating systems
  • Computer networks
  • Basic web development
  • Introductory AI / ML

Industry expects

  • Real product architecture
  • APIs and authentication
  • Cloud and Git workflows
  • Scalable applications
  • Debugging and system integration
  • LLM APIs, RAG and AI agents
  • Tool calling and MCP
  • Deployment and production thinking
  • Working with engineering teams

Our workshop connects these two worlds.

Workshop philosophy

Learn by building

This workshop is not primarily slides. Students should see applications being built in front of them.

20%

Concepts

Just enough theory to make the build make sense.

30%

Live engineering

Applications built in front of the room.

50%

Hands-on

Demos, interaction, and students in the loop.

“Don't just teach students what AI is. Show them how engineers actually build with AI.”

Curriculum

Four modules. One through-line: production AI.

Each module is taught as engineering practice — architecture, trade-offs and a working path — not as a slideshow of definitions.

  1. Module 01

    How Modern Web Applications Work

    Students understand how a real modern web application is structured.

    • Frontend vs backend
    • React / Next.js concepts
    • APIs and REST
    • Databases
    • Authentication
    • Client-server architecture
    • How frontend, backend and databases communicate
    • Production application architecture

  2. Module 02

    Understanding Generative AI

    Students learn how software engineers consume and integrate models — not how to derive the math.

    • What is an LLM?
    • Tokens and context windows
    • Prompting and inference
    • Hallucinations and model limitations
    • Model APIs
    • Structured outputs
    • Function / tool calling

  3. Module 03

    Build an AI-Powered Application

    Students see one simple application built end-to-end, live.

    • Frontend → backend → LLM → database / APIs
    • Live demonstration and hands-on application
    • Wiring an LLM into a real request path
    • Handling model responses in product UI

  4. Module 04

    RAG — Retrieval-Augmented Generation

    Students can explain how an AI answers from uploaded college documents instead of guessing.

    • Embeddings and vector databases
    • Document ingestion and chunking
    • Semantic search and context retrieval
    • Grounding AI answers
    • Reducing hallucinations
    • How companies use RAG for policies, docs, support and search

Major highlight

Model Context Protocol — giving AI tools

Instead of AI only answering questions, MCP allows AI systems to interact with tools and external systems.

Chatbots answer questions. AI systems with tools can perform work.

Architecture

AI / LLM
MCP
GitHub
Code
Database
Data
APIs
Services

Students learn

  • What MCP is, and why it exists
  • MCP client and MCP server
  • Tools, resources and prompts
  • How AI discovers tools
  • How models invoke tools
  • Security considerations

Live demonstration ideas

  • AI reads a GitHub repository
  • AI queries a database
  • AI creates or reads an issue
  • AI interacts with a custom API
  • Build a very simple MCP server

AI agents

From a single answer to a loop that can act

Students see why an agent is not “ChatGPT with extra steps” — and why most production systems still need a human in the loop.

  • What an AI agent is
  • Model + tools + memory + reasoning / workflows
  • Single-agent applications
  • Multi-step workflows
  • Agent limitations
  • Human approval
  • When agents should not be used
  1. 01

    Goal

  2. 02

    AI Agent

  3. 03

    Plan

  4. 04

    Use Tool

  5. 05

    Observe Result

  6. 06

    Reason

  7. 07

    Next Action

  8. 08

    Final Result

Real product engineering

What happens after the demo works?

Building a prototype is easy compared with building a production product. This is the differentiator most campus workshops skip.

AI Demo

A happy-path prompt. One happy-path screen. It works on the laptop.

AI Product

Auth, data, failures, cost, review, deploy, and someone on call when it breaks at 11pm.

  • Authentication and authorization
  • Databases and API design
  • Caching and rate limiting
  • Background jobs and queues
  • Observability and error handling
  • Security
  • Deployment and scaling
  • CI/CD and Git
  • Code review and testing

Corporate engineering exposure

How engineering teams actually work

The objective is to help students understand product-company engineering before their first job — not after they have already struggled through it.

Product ManagerDesignerFrontend EngineerBackend EngineerQADevOps / SREEngineering Manager
  1. 01Requirement
  2. 02Design
  3. 03Technical Design
  4. 04Development
  5. 05Code Review
  6. 06Testing
  7. 07Deployment
  8. 08Monitoring
  • Agile basics
  • Jira / tickets
  • Pull requests
  • Code reviews
  • Standups
  • Production incidents
  • Ownership
  • Communication

AI-assisted software engineering

Use AI as an engineer, not as a copier

Responsible AI coding is now a core skill. We treat it as craft, not as a shortcut around understanding.

  • AI coding assistants
  • Generating code
  • Reviewing generated code
  • Debugging with AI
  • Understanding existing repositories
  • Writing tests
  • Generating documentation
  • GitHub workflows
  • Why blindly copying AI-generated code is dangerous
The future engineer is not replaced by AI. The engineer who understands how to work effectively with AI becomes significantly more productive.

Optional advanced topics

Depth, when the room is ready

Selected with the HOD based on year, specialisation and prior exposure.

System Design
Microservices
Event-driven architecture
WebSockets
Cloud architecture
Docker
Vector databases
AI evaluation
LLM observability
AI security
Building SaaS applications
Startup product engineering

Two-day format

A schedule designed for attention, not coverage

Day 1 gets students from architecture to a working AI application. Day 2 moves from tools and agents to how product teams actually ship.

Day 1

Foundation to AI Application

  1. Session 1Modern software engineering and product architecture
  2. Session 2How LLMs and Generative AI work
  3. Session 3LLM APIs, prompting, structured outputs and tool calling
  4. Session 4Build a small AI-powered web application
  5. Session 5RAG, embeddings and vector databases
  6. Hands-onBuild / demonstrate a document-aware AI application

Day 2

From AI App to AI Engineer

  1. Session 1MCP architecture and MCP servers
  2. Session 2Live MCP server demonstration
  3. Session 3AI agents and tool-based applications
  4. Session 4Real-world product engineering
  5. Session 5AI-assisted development workflows
  6. Session 6Career roadmap + industry Q&A

Optional close: a mini challenge where students design or start an AI-powered product — then defend the architecture, not just the prompt.

Outcomes

By the end of the workshop students will understand

01

How modern web applications work

02

How LLM applications are built

03

How to integrate AI APIs

04

How RAG works

05

How vector databases work

06

How MCP servers work

07

How AI uses external tools

08

How agents work

09

How production AI applications differ from demos

10

How software teams operate

11

What skills product companies expect

12

How to start building their own AI projects

Who is this for?

Engineering students who will ship software

Recommended for 2nd / 3rd / 4th year. The curriculum is adjustable depending on skill level.

B.Tech CSECSE AI / MLArtificial IntelligenceData ScienceITMCAInterested ECE students

For principals, HODs and TPOs

Why host this workshop?

A professional, on-campus intervention that gives students industry exposure without replacing the department's own curriculum.

  • Industry exposure beyond the academic syllabus
  • Practical AI knowledge students can apply to projects
  • Understanding of modern engineering workflows
  • Hands-on exposure to live product architecture
  • Stronger final-year and mini-project quality
  • Better placement readiness for product companies
  • Awareness of current AI engineering trends
  • Direct interaction with practising engineers
  • Ability to start building real AI applications

Format

Built for a campus, not a webinar

Duration

1-Day / 2-Day

Delivery

On-Campus

Format

Interactive + Live Coding + Demonstrations

Audience

Engineering students

Batch Size

Flexible by institution

Curriculum

Customisable to college needs

Certificate

Certificate of Participation — Applied AI Engineering Bootcamp by EdZul

Issued to students who complete the workshop. This is a participation certificate from EdZul — not an accredited university or government certification.

EdZul

Applied AI Engineering Bootcamp

Certificate of Participation

On-campus · 1-day / 2-day · Industry-led

Institutions

Institutions we've engaged with

Workshop discussions currently underway with engineering institutions across Andhra Pradesh.

FAQ

Questions colleges usually ask

Is prior AI knowledge required?+

No. Basic programming understanding is enough for the standard workshop. We adjust depth for 2nd, 3rd and 4th year batches.

Is this workshop theoretical?+

No. It is built around live engineering, demonstrations and application architecture. Slides are supporting material, not the main event.

Can the workshop be customised?+

Yes. Curriculum can be adapted based on student year, specialisation and the college’s existing lab setup.

Do students need laptops?+

Recommended for hands-on sessions. We can also run a demonstration-led format when device access is limited.

How long is the workshop?+

Standard formats are one-day and two-day bootcamps. A custom schedule can be designed with the HOD or TPO.

Will students receive certificates?+

Students who complete the workshop can receive a Certificate of Participation — Applied AI Engineering Bootcamp by EdZul. This is a participation certificate, not an accredited degree credential.

Can faculty attend?+

Yes. Faculty participation is encouraged — it helps the department continue the conversation after the workshop.

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Bring Applied AI Engineering to your campus

Give your students exposure to the tools, architecture and engineering practices shaping the next generation of software products.

This is not another ChatGPT awareness seminar. Students get real industry and engineering exposure.

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Applied AI Engineering Bootcamp · On-campus workshops