BlockseBlock

AI Systems Architect - 30 Day Masterclass

RAG. Voice.
Agents. MCP.

Build AI agents. Launch your own app.

Recorded lessons. Weekly live mentoring.

Enrollment:Check availability

Next cohort:Date to be announced

Learn from Naureen Fathima,Data Scientist at JPMorgan Chase & Co.

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Course details

Masterclass
30 daysEnglish
Prerequisites
No prior codingneeded
Mode
Recorded
+ Live
Videos
On BlockseblockLMS · Self-paced
Live (required)
Every Sat · 1 hrGoogle Meet
Placement
6 monthsof support

What you’ll build
in 30 days.

RAG Knowledge
Assistant

Answers from your documents.

Voice AI Agent

Listens, responds and takes action.

MCP-Connected
Workflow

Connects agents to tools and context.

Your Own AI
Application

Combines it all in a live app.

Meet your mentor

Naureen Fathima

Data Scientist Associate at

JPMorgan Chase & Co.
Naureen explaining a topic on stage, wearing a headset and gesturing beside a presentation screen.
Naureen speaking in front of a JPMorgan Chase backdrop, with the seated audience in the foreground.
Naureen in an orange jacket, addressing the audience with an open-handed gesture on the JPMorgan Chase stage.

From Software Engineer II to Data Scientist at JPMorgan Chase, Naureen now designs and deploys multi-agent AI systems. Learn the engineering behind the AI applications you’ll build.

Co-presented AI Agents & MCP to 200+ attendees at JPMC Innovation Week.

Graduate studies

Georgia Tech

M.S. in Computer ScienceMachine Learning specialisation

“Just finished the capstone project today! … Naureen makes complex AI architecture look easy.”

Tarun JoshiQuestions? WhatsApp

Your learning community900+ learners

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  • Patakota Sudeep

    Associate Vice President

  • Rohit Kothari

    Data Engineer

    Group Digital Solutions India

  • Srinivas Rao

    Senior Technology Architect

  • Dipanjan Biswas

    Expert Data Scientist

  • Kapildas Panentavida

    Senior Engineer

  • Hardik Patil

    Performance Test Engineer

  • Abhi Vedwa

    Director

  • Sumit Chauhan

    Associate Manager

  • Veena Wale

    Senior Associate Technology L2

  • Shivashis Swain

    Senior Data Scientist

  • Ruhi Prabhakar

    ML & GenAI Project Lead

  • Sandeep Jadhav

    Assistant Consultant

  • Shiv Sai

    Senior Product Manager

Your 30-day
AI build sprint.

8 modules + a final project.
Start with the basics. Build your own AI app.

Module 01

Python, APIs & backend

How an AI backend worksA user app sends a request through a Python FastAPI service to an AI model. The service returns a structured response.Your appFastAPIPythonAImodelRequestStructured response
View detailsHide details for module 01: Python, APIs & backend

Build the foundation that connects an AI model to a working product.

  • Python & backend servicesWrite the logic that runs behind an AI application.
  • REST APIs & FastAPIConnect your application, backend and AI model through requests and responses.
  • End-to-end architectureMap how the parts of a simple AI product work together, from user input to output.

What you’ll ship

  • A micro AI service built with FastAPI.
  • An end-to-end architecture for a simple AI product.
Module 02

Developer tools & environments

A reproducible development environmentA project begins from a template, is tracked in Git and packaged with Docker. The package recreates the environment on different machines.TemplateGitrepositoryDockerYour machineAnother machine
View detailsHide details for module 02: Developer tools & environments

Set up a project you can track, reproduce and keep building on.

  • Git & version controlTrack changes and manage the code behind your AI service.
  • Docker environmentsPackage your service and its dependencies so the setup is reproducible.
  • Production-ready templateStart from the provided template repository and adapt it into your own project.

What you’ll ship

  • Your AI service containerized with Docker.
  • Your own project repository, set up from the production template.
Module 03

AI models & system design

Choosing a model for a use caseCompare large language models, small language models and vision-language models against cost, latency and context requirements. No performance rankings are implied.Your use caseLLMLarge textmodelSLMSmall textmodelVLMLanguage+ visionCostLatencyContext
View detailsHide details for module 03: AI models & system design

Choose the right model for the job, then design the system around it.

  • LLMs, SLMs & VLMsCompare large language models, small language models and vision-language models.
  • Real engineering trade-offsWeigh cost, latency and context-window limits against your use case.
  • Real product architecturesExplore the system patterns behind products such as ChatGPT, Perplexity and Siri.

What you’ll ship

  • A model-selection framework with cost and latency trade-offs.
  • A system diagram showing how a real AI product is built.
Module 04

RAG & knowledge systems

A retrieval-augmented generation pipelineFiles are chunked and embedded into a vector index. An embedded question retrieves relevant context. The question and retrieved context are given to the language model to produce an answer.FilesChunksEmbedVectorindexQuestionEmbed +retrieveContextLLMAnswer
View detailsHide details for module 04: RAG & knowledge systems

Give your AI the right information from your documents before it answers.

  • Document pipelines & chunkingPrepare documents and divide them into useful pieces for retrieval.
  • Embeddings & vector databasesRepresent document meaning and retrieve relevant information with semantic search.
  • Retrieval quality & accuracyTune the RAG architecture and retrieval performance to improve answer quality.

What you’ll ship

  • A Perplexity-style RAG knowledge assistant.
  • A REST API for the assistant, with retrieval performance optimized.
Module 05

Voice AI & autonomous agents

A voice agent with tool accessSpeech is transcribed and sent to an agent powered by a language model. The agent accesses external tools and memory through MCP. Its response is converted to spoken audio.SpeechSpeechto textAgent+ LLMMCPExternal tools+ memoryText tospeechVoice
View detailsHide details for module 05: Voice AI & autonomous agents

Build an agent that can listen, reason, use tools and respond aloud.

  • The complete voice pipelineConnect speech-to-text, a language model and text-to-speech into a working conversation.
  • Agent decisions & collaborationBuild agents that choose tools, take action and work together in multi-agent workflows.
  • Persistent memory & MCPConnect external tools and memory through MCP so the agent can use context beyond one exchange.

What you’ll ship

  • A fully voice-enabled AI agent.
  • External tool and persistent-memory integrations using MCP.
Module 06

Model Context Protocol architecture

MCP connects an AI application to external capabilitiesAn AI application hosts a model and an MCP client. The client exchanges context with an MCP server that exposes external tools and data. Memory is external storage, not built into the protocol.AI applicationAgent + modelMCP clientMCP serverToolsDocumentsMemory
View detailsHide details for module 06: Model Context Protocol architecture

Design how your AI application connects to tools, knowledge and memory.

  • MCP system architectureDesign the connections between your AI application, MCP clients and servers.
  • Context, memory & toolsLearn how AI systems coordinate access to these components at scale.
  • Across AI workflowsApply MCP to voice, autonomous agents and RAG systems, including context management between components.

What you’ll ship

  • A designed and implemented MCP-based AI system.
  • Context management connecting agents, memory and tools.
Module 07

Deployment & production engineering

Automated deployment and scalingA code change moves through testing and building to deployment. The cloud environment orchestrates multiple service instances to handle demand.CodeTest +buildDeployCloud · orchestrate & scaleServiceServiceService
View detailsHide details for module 07: Deployment & production engineering

Move beyond a local demo to an AI service running in the cloud.

  • End-to-end cloud deploymentTake your AI service from a development environment to a live cloud environment.
  • CI/CD pipelinesBuild an automated path for integrating changes and deploying new versions.
  • Orchestration & scalingManage containers and plan how your system handles real-world traffic.

What you’ll ship

  • An AI system deployed end-to-end in the cloud.
  • A CI/CD pipeline for automated deployment.
Module 08

AI security & monitoring

Security safeguards and observabilityAn illustrative flow shows input checks surfacing security risks while the AI service reports system health, performance and errors. Checks reduce risk; they are not a guarantee that all attacks are blocked.InputPromptchecksAIserviceFlagrisksHealth & errors
View detailsHide details for module 08: AI security & monitoring

Understand what your AI system is doing and build safeguards around it.

  • Observability & system healthMonitor the health and performance of your AI service.
  • AI-specific securityUnderstand risks that appear when models process inputs and interact with tools.
  • Prompt-injection defenceAdd protections against malicious instructions and other AI security risks.

What you’ll ship

  • Monitoring dashboards for system health.
  • Security protections that harden your AI system.
Final project

Your own AI application.

Your final integrated AI applicationRAG, Voice AI and agents combine in your application, with tools connected through MCP. The final deliverables include a live cloud deployment and architecture documentation.RAGVoice AIAgentsYour AI applicationAsk. Speak. Take action.MCPtoolsLive clouddeploymentArchitecturedocumentation
View detailsHide details for your final project

Bring the complete system together: RAG, Voice AI and agents connected through MCP.

Build from your own template repository. Design the architecture, connect the components, document your decisions and deploy the application to the cloud.

Choose your direction

  • Enterprise knowledge assistant
  • Voice-first AI copilot
  • Task automation agent
  • An AI product for your industry

What you’ll ship

  • A complete AI application with a live cloud deployment.
  • Full architecture documentation.

Tools used

Hover, touch and hold, or focus to pause the logos. Use the arrow keys to browse.

  • Python
  • FastAPI
  • Git
  • Docker
  • Model Context Protocol

Have questions about the masterclass?

Ask us on WhatsApp (opens in a new tab)

What our learners say.

Learning by doing

Vibhuti Devatraj0:27

In their own words

Tarun Joshi

Just finished the capstone project today! The bootcamp is very well structured. Naureen makes complex AI architecture look easy. extremely thankful for getting to learn from such an experienced person.

Siddharth Jain

Great program! The depth of knowledge in each of the videos and live session by the mentor Naureen is really good. The assignments actually make you think instead of just copy pasting code. the bootcamp is very well structured.

Megha Bhatia

the bootcamp is very well structured. I like how we went from basic python to full MCP deployment without getting overwhelmed. the depth of knowledge in each of the videos and live session by the mentor Naureen is just great.

Ankit Pandey

10/10 would recommend to my peers. The live Q&A was super helpful. I am so thankful for getting to learn from such an experienced person. the depth of knowledge in each of the videos and live session by the mentor Naureen is unmatched.

Rohit Chawla

it's rare to find a mentor who actually builds production systems. The bootcamp is very well structured and the way she breaks down MCP and agents is just wow. thankful for getting to learn from such an experienced person!

Amit Verma

tbh I was struggling with RAG concepts before taking this course. the bootcamp is very well structured. You can clearly see the depth of knowledge in each of the videos and live session by the mentor Naureen. I am just really thankful for getting to learn from such an experienced person.

Nidhi Kulkarni

Such a good course for IT folks looking to transition. Thankful for getting to learn from such an experienced person. The hands on projects are the best part. the depth of knowledge in each of the videos and live session by the mentor Naureen is solid.

Riya Malhotra

Learned a lot about voice AI which I didn't know much about before. The depth of knowledge in each of the videos and live session by the mentor Naureen is fantastic. bootcamp is very well structured.

Ishan Bhatnagar

I was looking for something beyond basic prompt engineering and this was exactly it. The bootcamp is very well structured. thankful for getting to learn from such an experienced person.

Rahul Mehta

Can't thank Naureen enough. The depth of knowledge in each of the videos and live session by the mentor Naureen helped me clear my doubts about AI deployment. totally recommend it.

Sneha Kapoor

I've taken a few other AI courses but nothing like this one. Highly recommend it to anyone looking to upskill. I am so thankful for getting to learn from such an experienced person. Naureen ma'am explains things perfectly.

Ananya Iyer

Loved the entire masterclass. The bootcamp is very well structured and doesn't just rush through the complex topics. I really appreciate the depth of knowledge in each of the videos and live session by the mentor Naureen. assignments are top notch.

Vikas Singh

i was a bit confused about vector DBs before starting this. I am very thankful for getting to learn from such an experienced person. Naureen ma'am's teaching style is superb and the depth of knowledge in each of the videos and live session by the mentor Naureen is amazing.

Pooja Sharma

The depth of knowledge in each of the videos and live session by the mentor Naureen is mind blowing honestly. Everything from backend to cloud deployment is covered perfectly. the bootcamp is very well structured.

Karan Desai

This was exactly what my career needed right now to move into AI. The bootcamp is very well structured especially the part about autonomous agents. thankful for getting to learn from such an experienced person.

Neha Gupta

I am mostly a backend dev so AI engineering seemed a bit tough initially. But thankful for getting to learn from such an experienced person, Naureen really knows her stuff inside out.

Divya Reddy

Worth the money guys. The whole 30 days masterclass is very well structured from start to finish. really thankful for getting to learn from such an experienced person.

Aakash Srivastava

If you want to build real world AI products, just go for this one. Very thankful for getting to learn from such an experienced person. Totally loved it, plus the bootcamp is very well structured.

Vivek Chauhan

I usually get bored in online courses but Naureen kept it so interesting. Thankful for getting to learn from such an experienced person. The concepts are explained so well.

Shruti Agarwal

Amazing experience. The depth of knowledge in each of the videos and live session by the mentor Naureen shows how much she has actually worked in the industry. the bootcamp is very well structured too.

Saurabh Mishra

The pace of the course is perfect, which really shows the bootcamp is very well structured. really thankful for getting to learn from such an experienced person.

Gaurav Khatri

The depth of knowledge in each of the videos and live session by the mentor Naureen really gave me a lot of confidence to start building my own AI agents. thankful for getting to learn from such an experienced person.

Kavita Nanda

Really good course. I am very thankful for getting to learn from such an experienced person. it helped me clear my basics and move to advanced RAG topics easily. depth of knowledge in each of the videos and live session by the mentor Naureen is really good.

Prateek Saxena

Honestly one of the best investments for my upskilling journey. The depth of knowledge in each of the videos and live session by the mentor Naureen is top class. the bootcamp is very well structured.

Aditi Thakur

The content is super relevant to what product companies want right now. The bootcamp is very well structured and I am genuinely thankful for getting to learn from such an experienced person.

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6 months of
placement support.

  • Portfolio projects

    Deployed AI projects
    to show your skills.

  • Résumé review

    1:1 mentor feedback
    on your résumé.

  • Talent & referrals

    Introductions via our
    HR & partner network.

Build towards your
next AI role.

Skills you’ll develop in the next 30 days.

AI Engineer

RAG, Agents, MCP,
Deployment

ML Engineer

Models, Pipelines,
Evaluation

Agent Engineer

Tool orchestration,
Memory, MCP

Voice AI Engineer

STT, TTS,
Multimodal systems

AI Product Engineer

System design, UX,
Scalability

What your fee
gets you.

Swipe or drag to compare

Our masterclass compared with four illustrative price tiers, including duration of programme. Checks show offered features; crosses show features not confirmed as included in reviewed examples.
What’s
included		Video~₹500CoursesBootcamps₹5k–15kProgramsOursRegular price ₹11,999Current price ₹7,499MasterclassCohorts₹15k–50kProgramsMentored₹50,000+Programs
Duration of programmeSelf-paced5 months30 days8–13 weeksSeveral months
Live mentor sessions
1:1 guidance
RAG, Voice,
Agents & MCP
Self-paced videos
No coding prerequisite
Placement support
Offered Not confirmed included

Frequently asked
questions

I’m new to coding. Can I follow this in 30 days?

Yes. No prior coding experience is needed. The 30 days give you a guided path from the basics to building a working AI app. Revisit recorded lessons at your pace and bring questions to weekly live mentoring. Set aside time for practice and project work between live sessions.

Can I do this alongside work?

Yes. Set aside 1 hour every Saturday for the required live session. Watch lessons and build projects at your pace during the 30-day masterclass. That self-paced work is in addition to the live hour.

How are the lessons delivered?

Learn in English through recorded videos on the BlockseBlock LMS, plus weekly live sessions on Google Meet for mentoring and build-alongs.

What’s included in the fee?

The current fee is ₹7,499, reduced from ₹11,999. It includes all 8 modules, the final project, recorded lessons, live mentoring, and 6 months of placement support.

What will I build in 30 days?

You’ll build a retrieval-augmented generation (RAG) assistant that answers from your documents, a voice AI agent, and a workflow connecting AI agents to tools through Model Context Protocol (MCP). You’ll combine them in a deployed AI application. Explore the syllabus.

How do I get help when I’m stuck?

Bring questions to weekly live mentoring and get 1:1 guidance as you build. You can also connect with the learner community throughout your learning journey.

What does placement support include?

6 months of support with portfolio projects, résumé reviews, and introductions through our talent and referral network.

Will I receive a certificate?

Yes. Complete the masterclass to earn your BlockseBlock AI Systems Architect training completion certificate. Add it to LinkedIn and show it alongside your projects.

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Ask us on WhatsApp (opens in a new tab)

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