Innoskrit
AI-First Software Engineering Program

A Product-Building
Curriculum Journey

Students learn Java, DSA, Problem-Solving, Full Stack Engineering, AI, and System Design by repeatedly improving two real systems: a Feature Flag System and Trello.

300 Hours2 Portfolio Systems120+ DSA Problems
300hours

Java, DSA, Full Stack, AI, and Design

2systems

Feature Flag System and Trello

120+problems

Curated DSA and Interview Drills

1journey

Learn, Build, Add AI, Scale, and Launch

The project spine

Two products. One curriculum. AI added where it belongs.

We teach basics first, then use those basics to build real systems. Once the products are working, the AI course extends those same products with useful AI capabilities instead of disconnected demos.

Product build
Flags
Checkout redesign
AI board summary
Beta reports
Trello
Todo
Doing
Done
AI is added after the systems work: summaries, rule generation, risk analysis, semantic search, and human-reviewed suggestions.
Release safely

Feature Flag System

A LaunchDarkly-style platform for controlled product releases.

Students build a production-ready feature management platform where teams can create flags, target users, roll out features gradually, track changes, and safely turn functionality on or off without redeploying code.

JavaSpring BootPostgreSQLNext.jsWebSocketsAWS

Students build

  • Flag dashboard with projects, environments, owners, tags, and lifecycle states.
  • Rule engine for user segments, percentage rollouts, prerequisites, and kill switches.
  • Evaluation API that applications can call to decide whether a feature is enabled.
  • Audit logs, approvals, metrics, and release history for engineering teams.

AI extensions

  • Natural-language flag creation that converts release intent into targeting rules.
  • Rollout risk analyzer that explains blast radius before a launch.
  • Experiment and incident summarizer for product, engineering, and leadership updates.
A portfolio-grade backend-heavy SaaS product that proves API design, database modeling, auth, testing, observability, and product thinking.
Plan clearly

Trello

A collaborative kanban workspace for planning real engineering work.

Students build a Trello-inspired project management product with boards, lists, cards, members, comments, labels, activity timelines, and real-time collaboration, then evolve it into an AI-assisted planning system.

JavaSpring BootPostgreSQLNext.jsDrag and DropWebSockets

Students build

  • Boards, lists, cards, labels, due dates, attachments, comments, and member roles.
  • Drag-and-drop workflows with ordering, optimistic UI, and conflict handling.
  • Activity feeds, notifications, filters, search, and team permissions.
  • Reusable frontend components for dense product workflows.

AI extensions

  • Task breakdown assistant that converts a vague goal into cards, checklists, and owners.
  • Board summarizer that explains status, blockers, overdue work, and next actions.
  • Semantic card search and priority suggestions using embeddings and workspace context.
A collaboration product that demonstrates frontend depth, real-time systems, clean UX, access control, and AI features grounded in actual user workflows.
Full syllabus

The journey from fundamentals to AI-powered product systems.

Every module has clear outcomes, granular topic descriptions, problem-solving practice, and a project milestone tied back to the systems students are building.

01
Foundation25 Hours

Programming with Java

From syntax to backend-ready Java

Start with Java as the engineering language for the program. Students learn to write clean, testable code that later becomes the backend foundation for the Feature Flag System and Trello.

Module outcome

Students create small Java services and domain models that later become flags, users, boards, cards, and permissions.

Students can
  • Write clean Java programs with strong fundamentals.
  • Model real-world entities using OOP and SOLID basics.
  • Use collections, exceptions, files, streams, and tests confidently.

Java Fundamentals

Get comfortable with the language, tooling, debugging, and the mental model of writing programs that are easy to reason about.

Setup, IDE, Git, and Debugging

Install Java 17+, configure IntelliJ, run programs from the terminal, use breakpoints, inspect variables, and commit work cleanly.

Variables, Types, Operators, and Input

Understand primitive types, strings, casting, expressions, console input, and how type choices affect correctness.

Control Flow and Loops

Use conditionals, loops, switch expressions, guard clauses, and dry runs to translate requirements into working logic.

Methods and Recursion

Break code into reusable functions, reason about call stacks, and solve simple recursive problems before DSA begins.

Arrays, Strings, and Debug Tracing

Manipulate indexed data, trace loops, handle edge cases, and build confidence with the structures used heavily in DSA.

Object-Oriented Programming

Move from writing scripts to designing domain models for real products.

Classes, Objects, and Constructors

Represent products, users, permissions, cards, and flags as objects with meaningful state and behavior.

Encapsulation and Immutability

Protect state, expose safe APIs, and learn when immutable objects reduce bugs in product code.

Inheritance, Interfaces, and Polymorphism

Use abstractions for extensible behavior such as notification channels, flag rules, and card actions.

SOLID Principles Introduction

Apply single responsibility, dependency inversion, and open-closed thinking without over-engineering beginner code.

Collections and Generics

Learn the data containers used in backend services and problem solving.

List, Set, and Map

Choose the right collection for ordering, uniqueness, lookups, membership checks, and grouping.

ArrayList, LinkedList, HashSet, HashMap, and TreeMap

Compare behavior, performance, common operations, and real use cases in APIs and algorithms.

Generics and Type Safety

Design reusable containers, service responses, repositories, and utilities without losing compile-time safety.

Sorting and Comparators

Sort cards, releases, users, and problem inputs using custom ordering and comparator composition.

Errors, Files, Testing, and Modern Java

Round out the foundation with reliability habits that carry into every project.

Exception Handling

Use checked and unchecked exceptions, custom errors, and consistent failure messages for predictable APIs.

File Handling and Serialization

Read, write, parse, and persist simple data before moving to databases.

JUnit Basics and Testable Code

Write small unit tests, structure assertions, and catch regressions before code reaches larger systems.

Streams, Optional, Records, and Lambdas

Use modern Java features for transformations, null safety, DTOs, and concise business logic.

Concurrency Basics

Understand threads, executors, synchronization, and CompletableFuture at a practical backend level.

02
Problem Solving100 Hours

DSA and Problem Solving

Patterns, dry runs, and interview reps

Build interview-grade problem solving in Java with a structured set of patterns, dry runs, complexity analysis, and curated coding practice instead of only listing data structures.

Module outcome

Students reuse DSA thinking in product features such as rule evaluation, search, sorting, graph-like permissions, recommendations, and scheduling.

Students can
  • Solve 120+ curated problems across core DSA patterns.
  • Explain time and space complexity in interviews.
  • Convert unknown questions into familiar patterns through practice.

Arrays, Strings, and Searching

Start with the highest-frequency interview patterns and build a habit of edge-case driven coding.

Practice prompts
  • Two Sum, 3Sum, Product of Array Except Self
  • Best Time to Buy and Sell Stock, Merge Intervals
  • Search in Rotated Sorted Array, Find First and Last Position
  • Longest Substring Without Repeating Characters, Minimum Window Substring
Array Traversal and In-Place Updates

Handle indexes, mutation, boundary checks, and single-pass transformations.

Move ZeroesRotate ArrayMerge Sorted Array
Prefix Sums and Difference Arrays

Answer range queries, build counters, and reason about cumulative state efficiently.

Subarray Sum Equals KRange Sum QueryCar Pooling
Binary Search Patterns

Search sorted data, answer boundary questions, and apply binary search on the answer space.

Search Insert PositionKoko Eating BananasCapacity to Ship Packages
String Windows and Pattern Matching

Use frequency maps, two pointers, and sliding windows for substring and matching problems.

Valid AnagramGroup AnagramsPermutation in String

Pointers, Linked Lists, Stacks, and Queues

Learn pointer movement and state-machine style reasoning, then apply it to classic interview prompts.

Practice prompts
  • Reverse Linked List, Detect Cycle, Merge Two Sorted Lists
  • Valid Parentheses, Min Stack, Daily Temperatures
  • Sliding Window Maximum, LRU Cache
Two Pointers and Fast-Slow Pointers

Solve pair, cycle, partition, and middle-node problems with clear pointer invariants.

Container With Most WaterLinked List CyclePalindrome Linked List
Linked List Manipulation

Reverse, merge, split, rotate, and delete nodes while avoiding null pointer mistakes.

Remove Nth NodeReorder ListSort List
Stack and Monotonic Stack

Model nested state, previous/next greater elements, expression parsing, and rollback behavior.

Asteroid CollisionLargest Rectangle in HistogramNext Greater Element
Queues, Deques, and Heaps

Handle scheduling, BFS frontiers, top-k queries, and priority-based processing.

Top K Frequent ElementsKth Largest ElementTask Scheduler

Hashing, Recursion, Backtracking, and Greedy

Develop the ability to prune search spaces and make locally optimal decisions with proof-oriented thinking.

Practice prompts
  • Subsets, Permutations, Combination Sum
  • Word Search, N-Queens, Generate Parentheses
  • Jump Game, Gas Station, Non-overlapping Intervals
HashMap and HashSet Patterns

Use frequencies, seen-state tracking, grouping, and lookup tables to reduce nested loops.

Longest Consecutive SequenceContains DuplicateIsomorphic Strings
Recursion and Backtracking Templates

Build choice trees, track visited state, undo decisions, and explain pruning.

Subsets IIPalindrome PartitioningWord Search
Greedy Strategy

Recognize when local choices are safe and communicate the proof in interview language.

Merge IntervalsMinimum Number of ArrowsPartition Labels
Complexity and Dry Runs

Practice writing constraints-first solutions, table traces, and interview-ready explanations.

Trees, Graphs, Tries, and Union-Find

Move into non-linear structures used in both interviews and real-world product systems.

Practice prompts
  • Binary Tree Level Order Traversal, Lowest Common Ancestor
  • Number of Islands, Clone Graph, Course Schedule
  • Dijkstra, Network Delay Time, Redundant Connection
  • Implement Trie, Word Search II
Tree Traversals and BSTs

Use DFS, BFS, recursion, iterative traversal, and BST ordering to solve structural problems.

Validate BSTDiameter of Binary TreeKth Smallest in BST
Graphs and Topological Sort

Represent dependencies, traverse components, detect cycles, and solve ordering problems.

Course ScheduleRotting OrangesPacific Atlantic Water Flow
Shortest Paths and MST Basics

Learn Dijkstra, Bellman-Ford intuition, and weighted graph reasoning for routing-style problems.

Network Delay TimeCheapest Flights Within K StopsMin Cost to Connect Points
Tries and Union-Find

Apply prefix trees and disjoint sets to search, autocomplete, connectivity, and grouping problems.

Design Add and Search WordsAccounts MergeNumber of Provinces

Dynamic Programming

Build DP from first principles: state, transition, base case, order, and optimization.

Practice prompts
  • Climbing Stairs, House Robber, Coin Change
  • Longest Common Subsequence, Edit Distance
  • 0/1 Knapsack, Partition Equal Subset Sum
  • DP on grids, strings, trees, and intervals
Memoization and Tabulation

Convert brute force recursion into cached state and then into bottom-up tables.

FibonacciMin Cost Climbing StairsDecode Ways
1D and 2D DP

Model choices over indexes, capacities, grids, and subsequences.

Coin ChangeUnique PathsLongest Increasing Subsequence
String DP

Solve subsequence, edit, palindrome, and matching problems with clear state definitions.

LCSEdit DistanceLongest Palindromic Subsequence
Interview DP Drills

Practice recognizing DP signals, explaining transitions, and avoiding memorization-only learning.

03
Build Products85 Hours

Full Stack Product Engineering

Feature Flag System + Trello

Build the two core portfolio systems: a Feature Flag System and a Trello-style collaboration product. The goal is not toy CRUD, but real product architecture with auth, data modeling, testing, and deployment.

Module outcome

By the end of this module, students have both core systems working without AI. These become the base products for the AI module.

Students can
  • Build production-style APIs with Spring Boot and PostgreSQL.
  • Create polished Next.js product interfaces for dense workflows.
  • Ship two deployable systems with auth, tests, CI/CD, and cloud basics.

Spring Boot Backend

Design APIs that are organized, testable, secure, and ready for real product workflows.

Project Structure and Dependency Injection

Set up layered architecture with controllers, services, repositories, DTOs, validators, and configuration.

REST API Design

Design resources, status codes, pagination, filtering, versioning, and consistent error contracts.

Validation and Exception Handling

Protect the system with request validation, domain validation, global exception handlers, and useful error responses.

Testing with JUnit, Mockito, and Testcontainers

Write unit, integration, and repository tests around business rules, APIs, and database behavior.

Database and Domain Modeling

Turn product requirements into schemas that survive real usage and growth.

PostgreSQL Schema Design

Model users, workspaces, roles, flags, environments, boards, lists, cards, labels, and activity logs.

JPA and Hibernate

Use entities, relationships, transactions, projections, lazy loading, and query patterns safely.

Migrations with Flyway

Version database changes and manage schema evolution as the projects grow.

Indexes and Query Optimization

Add indexes for search, filters, rule evaluation, activity feeds, and board performance.

Authentication, Authorization, and Multi-Tenancy

Teach the product who the user is, what they can access, and which workspace they belong to.

JWT Authentication

Implement login, refresh tokens, secure password handling, and authenticated API flows.

Spring Security

Secure endpoints, configure filters, apply method-level permissions, and handle unauthorized access.

Role-Based Access Control

Model owners, admins, members, viewers, card assignees, and flag approvers.

Workspace Isolation

Prevent cross-tenant data leaks across organizations, projects, boards, and environments.

Next.js Frontend

Build product interfaces that feel useful, fast, and organized instead of demo-like.

React Fundamentals and Hooks

Build composable UI components for dashboards, forms, tables, drawers, cards, and activity feeds.

State Management and Data Fetching

Use local state, context, caching, optimistic updates, and error states for real workflows.

Forms and Validation

Create multi-step forms for flags, rollout rules, cards, checklists, labels, and permissions.

Drag and Drop Product UX

Build Trello-style board interactions with stable ordering, keyboard-friendly flows, and optimistic UI.

Realtime, Deployment, and Delivery

Move from local project to deployable system with collaboration and delivery discipline.

WebSockets and Live Updates

Broadcast card moves, comments, flag changes, rollout status, and collaboration activity.

Docker and Environment Config

Containerize frontend, backend, and database with local and cloud-ready configuration.

CI/CD with GitHub Actions

Run tests, builds, lint checks, and deployment steps consistently on every change.

AWS Basics

Deploy with EC2, RDS, S3, domains, HTTPS, environment variables, and basic monitoring.

04
Add AI60 Hours

AI Engineering for Product Systems

AI inside the same two systems

Learn AI basics first, then apply them only inside the Feature Flag System and Trello. Students do not build disconnected AI demos; they add AI capabilities to the products they already understand.

Module outcome

The same two products become AI-assisted systems: smarter releases, smarter planning, smarter search, and smarter summaries.

Students can
  • Use LLM APIs, prompting, embeddings, RAG, tools, and agent workflows.
  • Add AI features to the Feature Flag System and Trello with production guardrails.
  • Evaluate AI behavior for quality, cost, latency, and safety.

Python and LLM Foundations

Build enough AI engineering literacy to integrate models into software products responsibly.

Python for AI Workflows

Use Python functions, classes, modules, virtual environments, HTTP clients, async basics, and data processing.

How LLMs Work in Products

Understand tokens, context windows, temperature, latency, cost, streaming, and failure modes.

Model APIs and SDKs

Call LLM APIs, handle retries, stream responses, manage keys, and design clean service boundaries.

Structured Outputs

Force useful JSON responses for rule drafts, card creation, summaries, checklists, and risk reports.

Prompting, RAG, and Tool Use

Teach models to work with product context instead of giving generic chatbot answers.

Prompt Design and Evaluation

Write instructions, examples, rubrics, and regression checks for product-specific AI behavior.

Embeddings and Semantic Search

Index cards, comments, flag docs, release notes, and incident history for meaning-based retrieval.

RAG Pipelines

Retrieve relevant workspace context, ground model responses, cite sources, and reduce hallucination.

Function Calling and Tools

Let AI safely call product actions such as draft flag rule, create cards, summarize board, or search history.

AI in the Feature Flag System

Add AI features that help teams launch software safely.

Natural-Language Flag Builder

Convert prompts like launch to beta users in Bengaluru at 10 percent into structured targeting rules.

Rollout Risk Analyzer

Use audience size, environment, dependencies, recent incidents, and usage metrics to explain release risk.

Experiment and Incident Summaries

Summarize flag history, metric changes, rollout events, comments, and actions into stakeholder-ready updates.

AI Guardrails for Releases

Add approval gates, confidence checks, auditability, and human review before AI-assisted launch changes.

AI in Trello

Add AI features that help teams plan, track, and explain work.

Task Breakdown Assistant

Turn a vague project goal into cards, checklists, labels, owners, and milestones using structured outputs.

Board and Sprint Summaries

Summarize done work, blockers, overdue tasks, stale cards, ownership gaps, and next actions.

Semantic Card Search

Find related cards and comments by meaning, even when users do not remember exact keywords.

Priority and Deadline Suggestions

Use due dates, labels, dependencies, and activity history to recommend what needs attention.

Production AI Systems

Make AI features shippable, observable, and safe enough for real users.

Tracing and Observability

Log prompts, retrieved context, model outputs, latency, cost, and user feedback for debugging.

Evaluation Suites

Create test cases for flag rules, card generation, summarization quality, and unsafe responses.

Cost and Latency Optimization

Choose models, cache outputs, stream responses, batch requests, and set fallbacks thoughtfully.

Human-in-the-Loop UX

Design AI suggestions as drafts users can inspect, edit, approve, reject, and audit.

05
Scale and Design30 Hours

Machine Coding and System Design

Design interviews from real products

Use the same product systems to learn low-level design, high-level design, scalability, queues, caching, and interview case studies. Students design systems they have actually built.

Module outcome

Students redesign Feature Flags and Trello for scale, reliability, multi-tenancy, realtime collaboration, and AI workloads.

Students can
  • Break vague requirements into clean classes, APIs, and workflows.
  • Design scalable architectures for collaboration, releases, search, and AI features.
  • Practice machine coding and system design interviews with stronger product intuition.

Machine Coding

Practice implementation-heavy design problems under interview constraints.

Problem Decomposition

Clarify requirements, identify entities, define APIs, plan data structures, and sequence implementation.

Parking LotSplitwiseBookMyShow
Clean Code and Extensibility

Write readable code with clear naming, small classes, low coupling, and test-friendly boundaries.

LLD Patterns in Product Features

Design flag rules, board actions, notification systems, rate limiters, and audit logs.

Feature Flag Rule EngineTrello Activity FeedRate Limiter
Timed Machine Coding Rounds

Practice scope control, incremental delivery, test cases, and final walkthroughs.

Design Patterns

Learn patterns through the two product systems instead of isolated textbook examples.

Strategy and Chain of Responsibility

Apply to targeting rules, notification routing, policy checks, and AI tool selection.

Factory, Builder, and Adapter

Create DTOs, model clients, card templates, flag templates, and provider integrations safely.

Observer and Command

Model activity feeds, undo/redo, audit logs, board events, and release events.

Repository and Service Boundaries

Keep business rules separate from persistence, APIs, and external services.

High-Level System Design

Scale the exact systems students have built, then generalize the patterns to interviews.

Scalability Fundamentals

Estimate traffic, storage, throughput, latency, read/write paths, and bottlenecks.

Caching and CDN Strategy

Cache flag evaluations, board metadata, search results, static assets, and AI outputs.

Queues and Event-Driven Architecture

Use async events for notifications, audit logs, AI summaries, indexing, and metrics ingestion.

Database Scaling

Compare indexes, partitioning, replication, sharding, read replicas, and denormalized views.

Reliability and Observability

Design health checks, alerts, tracing, retries, idempotency, and fallback behavior.

Interview Case Studies

Practice classic designs after building enough context to make answers concrete.

Practice prompts
  • Design Feature Flag Platform
  • Design Trello or Jira
  • Design URL Shortener
  • Design Instagram Feed
  • Design WhatsApp
  • Design Uber or Ola
Feature Flag Platform at Scale

Design low-latency evaluations, SDK caching, rule propagation, auditability, and kill switches.

Trello or Jira at Scale

Design boards, permissions, realtime updates, ordering, search, notifications, and collaboration.

AI Feature Architecture

Design RAG services, prompt stores, eval pipelines, vector search, and human approval flows.

Mock Design Interviews

Practice clarifying questions, diagrams, tradeoffs, bottlenecks, and final deep dives.

06
LaunchOngoing

Career Launchpad

Portfolio, interviews, and placement

Turn the journey into career outcomes with portfolio polishing, interview practice, resumes, LinkedIn, GitHub, referrals, and company-specific preparation.

Module outcome

Students convert their systems into case studies with architecture diagrams, demos, metrics, and interview stories.

Students can
  • Present the Feature Flag System, Trello, and AI extensions as strong portfolio work.
  • Practice coding, machine coding, system design, and behavioral interviews.
  • Build a credible job-search engine across resume, GitHub, LinkedIn, and referrals.

Portfolio Packaging

Make the projects easy for recruiters and interviewers to understand quickly.

Project READMEs and Case Studies

Document problem statement, architecture, features, tradeoffs, screenshots, setup, and demo links.

GitHub Hygiene

Clean commits, clear branches, issue tracking, tests, environment examples, and meaningful project boards.

Architecture Diagrams

Explain service boundaries, database design, auth, realtime updates, AI pipelines, and deployment.

Demo Storytelling

Practice a 3-minute and 10-minute walkthrough of both products and their AI capabilities.

Resume, LinkedIn, and Personal Brand

Translate learning into signals that hiring teams can trust.

Resume Reviews and Rewrites

Write project bullets with impact, technical depth, scale, ownership, and measurable outcomes.

LinkedIn Profile Optimization

Improve headline, about section, featured projects, proof of work, and recruiter search keywords.

GitHub Portfolio Positioning

Pin the strongest repositories and make technical depth visible before an interview starts.

Public Learning Posts

Turn project decisions, DSA learnings, and AI experiments into credible short posts.

Interview Preparation

Practice the rounds students will actually face.

Mock Coding Interviews

Run timed DSA rounds with feedback on approach, communication, code quality, and edge cases.

Machine Coding Interviews

Practice scoped product features, clean code, tests, and extensible design under time pressure.

System Design Interviews

Use Feature Flags, Trello, and classic systems to practice tradeoffs, scale, and reliability.

Behavioral Interview Prep

Use STAR stories from projects, teamwork, debugging, ownership, conflict, and learning moments.

Mentorship and Placement Support

Keep students accountable through the job search instead of stopping at course completion.

1:1 Mentor Reviews

Get guidance on project depth, interview readiness, applications, and weak areas.

Company-Specific Prep

Prepare for patterns, hiring rounds, and expectations at product companies and startups.

Referral Network Access

Use proof of work and mentor feedback to approach referrals with stronger credibility.

Offer and Negotiation Basics

Understand compensation conversations, timelines, competing offers, and professional communication.

Bring this curriculum to your campus.

We can walk you through the project journey, module structure, mentorship model, and how students graduate with interview practice plus two serious portfolio systems.