AI Web Studio · Level 3 — Architect

AI Product Engineer

From Application to Production AI Product

I can understand, experiment with and build production-oriented AI products.

Take Self-Assessment Projects & capstone
OverviewDay-by-dayProjectsFees
35Days
7Weekly checkpoints
2Mini projects
1Market-oriented capstone
Tools & concepts you’ll work with
Docker Redis Kubernetes Node.js React Queues & events Observability RAG & agents
Curriculum

Your 35-day plan

Every day has one clear learning outcome, and every week ends with a checkpoint you must be able to explain and demonstrate.

Week1
System Foundation
Days 1–5

Goal: Think in systems: requirements, scale, bottlenecks and failure.

  1. Day01

    Translate product needs into functional and non-functional requirements.

  2. Day02

    Estimate traffic, storage, latency, availability and growth.

  3. Day03

    Design high-level architectures with services, databases, caches and queues.

  4. Day04

    Identify bottlenecks and single points of failure.

  5. Day05

    Use AI to challenge architecture decisions.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Week2
Distributed Application Engineering
Days 6–10 Mini Project 1

Goal: Understand communication, caching and distributed reliability.

  1. Day06

    Explain synchronous vs asynchronous communication.

  2. Day07

    Understand queues, events, workers, retries, timeouts and idempotency.

  3. Day08

    Understand Redis and caching as practical building blocks.

  4. Day09

    Experiment with TTL, eviction and cache-aside behaviour.

  5. Day10

    Design for partial failure.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Mini Project 1 is introduced this week — you choose one idea, then build it over two independent studio days. See project details
Week3
AI Product Engineering
Days 11–15

Goal: Build AI features as production system components.

  1. Day11

    Understand RAG from ingestion to retrieval to generation.

  2. Day12

    Understand chunking, embeddings, vector search and metadata.

  3. Day13

    Design AI evaluation for correctness, relevance, groundedness and task success.

  4. Day14

    Understand agents, tools, permissions, state and failure boundaries.

  5. Day15

    Reason about model choice, latency, tokens, cost and fallbacks.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Week4
Scale, Secure & Observe
Days 16–20 Mini Project 2

Goal: Make production systems scalable, secure and diagnosable.

  1. Day16

    Understand horizontal scaling, load balancing and stateless services.

  2. Day17

    Design authentication, authorization, secrets and service boundaries.

  3. Day18

    Understand logs, metrics, traces, dashboards and SLO/SLI concepts.

  4. Day19

    Reason about incidents, cascading failures and graceful degradation.

  5. Day20

    Diagnose behaviour using observability evidence.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Mini Project 2 is introduced this week — you choose one idea, then build it over two independent studio days. See project details
Week5
Cloud, Delivery & Product Optimization
Days 21–25

Goal: Connect architecture to deployment and business economics.

  1. Day21

    Understand containers, Docker and orchestration concepts.

  2. Day22

    Understand CI/CD, rollout, rollback and environment separation.

  3. Day23

    Estimate infrastructure and AI cost drivers.

  4. Day24

    Evaluate reliability, latency, scalability, security, effort and cost.

  5. Day25

    Use product/operational analytics to decide what to optimize.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Week6
Capstone & Production Build
Days 26–30

Goal: Build the core production AI product and integrate the architecture.

  1. Day26

    Turn a real problem into a production-oriented AI product plan.

  2. Day27

    Implement the capstone's core user journey and backend/data flow.

  3. Day28

    Integrate AI, RAG/agent capabilities and distributed workflows where appropriate.

  4. Day29

    Connect observability, security and production configuration.

  5. Day30

    Prepare the product for hardening, testing and architecture review.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Week7
Production Hardening, Failure & Productization
Days 31–35 Capstone

Goal: Break, harden, evaluate and defend the production AI product.

  1. Day31

    Harden the capstone against performance, reliability and operational weaknesses.

  2. Day32

    Review the architecture at 10×/100× scale and identify redesign needs.

  3. Day33

    Run failure experiments across database, cache, workers, APIs and AI dependencies.

  4. Day34

    Apply AI quality controls and guardrails: grounding, validation, prompt-injection protection, permissions and human approval.

  5. Day35

    Defend architecture and product decisions, including cost/user, usage limits, monetization and the next evolution of the system.

Checkpoint: The student should be able to explain the ideas in their own words, use AI to implement or investigate them, inspect the result, and make a meaningful change.
Capstone — a dedicated production and planning day, then you build independently and return for review after one week. See project details
How every session works

The studio pattern behind all 35 days

  1. story
  2. learn
  3. AI use
  4. live build
  5. analyze
  6. experiment
  7. streamline

Our AI philosophy

Students use AI to accelerate implementation, but must understand the important generated code/configuration and test changes.

Projects

Build, ship and defend your work

Two mini projects and a market-oriented capstone. You use AI to build faster — and you explain the important generated code and decisions.

How Mini Project 1 works

Students choose one idea. The project is introduced with 30 extra minutes on the introduction day. After introduction, use two independent studio work days for implementation; these are student build days rather than new lecture sessions.

Minimum requirements

  • Solve a clear user problem.
  • Use the concepts covered before the project introduction.
  • Use AI for at least one research/design/build/debug/review task.
  • Keep an AI context/prompt log.
  • Demonstrate a working POC.
  • Explain the important generated code and one thing that was changed after analysis.

Submission checklist

  • Problem statement
  • Target user
  • Sitemap or architecture (depending on level)
  • Working POC
  • AI prompt/context notes
  • One before/after improvement
  • Known limitation
  • 2-minute demo

Pick one idea · 30 options

  1. Local business landing page
  2. College club website
  3. Restaurant menu + enquiry
  4. Fitness studio site
  5. Photography portfolio
  6. Freelancer portfolio
  7. Event registration landing page
  8. Travel destination guide
  9. Pet-care service site
  10. Home tutor profile
  11. Student resume site
  12. NGO awareness site
  13. Local bakery site
  14. Salon appointment UI
  15. Real-estate property showcase
  16. Wedding invitation site
  17. Small hotel website
  18. Car/bike service centre site
  19. Interior designer portfolio
  20. Architect portfolio
  21. Digital marketing agency site
  22. Campus fest site
  23. Book club site
  24. Music teacher site
  25. Coaching institute landing page
  26. Local doctor information site (no medical advice feature)
  27. Art/illustration portfolio
  28. Startup product landing page
  29. Community sports club site
  30. Local tourism micro-site
Fees

Invest in Level 3 - AI Product Engineer

One program, two ways to enrol. Students with a valid college ID save ₹10,000 on the same 35-day program.

Save ₹10,000Student fee₹40,000 + GSTWith a valid college ID · same program
Regular fee₹50,000 + GST35 days · 2 mini projects · capstone
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AI Web Studio path

Where this level fits

Level 1 · Build ← PreviousAI Web BuilderLevel 2 · Engineer ← PreviousAI Full-Stack Developer
Level 3 · You are hereAI Product Engineer
Your mentor

Learn From Someone Who Builds Systems at Scale

Your mentor has spent 15+ years architecting enterprise infrastructure and automation, so your guidance on building, deploying and scaling real products comes from production experience.

Sekhar
Connect on LinkedIn

Sekhar

  • Co-founder, Zenzo AI (zenzolabs.com)
  • Infrastructure Architect
  • Automation Leader
  • AI Builder

15+ years of experience architecting scalable, secure and automated infrastructure platforms. Specialized in virtualization, cloud automation and agentic AI to drive autonomous operations at enterprise scale.

15+Years experience
6+Global enterprises
1000+VMs managed at scale
40%+Manual effort reduced
Expertise
Cloud & VirtualizationAutomation & OrchestrationAgentic AI & LLMsCompliance & ReliabilityObservability & Analytics

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