DSA, System Design & AI Career Accelerator for Working IT Professionals
The Complete 2026 SDE Interview Roadmap
Master DSA, System Design, and AI for SDE interviews — the only program in India covering everything the top product companies now test. Get your profile optimized and land your dream offer.
Detailed Syllabus
6 modules covering everything the top product companies now test — DSA, System Design, and AI
- Arrays & Maths: Subarray problems, Prefix Sum, Number Theory
- Two Pointers: Opposite/Same direction pointers, 3-pointer technique
- Bit Manipulation: Basic Ops, Bit Masking, Hamming Distance
- Searching: Linear Search, Binary Search & Variations
- Sorting: Bubble, Selection, Insertion, Quick Sort, Merge Sort
- Strings: String Manipulation, Matching Algorithms
- Backtracking: Recursion, Permutations & Combinations
- Hashing: Collision Resolution, Hash Tables, Applications
- Stacks: Implementation, Expression Evaluation, Histograms
- Queues: Circular Queue, Priority Queue, Deque
- Linked Lists: Types, Implementation, Pointer-based questions
- Trees: Traversals (In/Pre/Post/Level), Lowest Common Ancestor
- BST: Time/Space analysis, Insertion/Deletion, Traversals
- Tries: Basic Operations, Word Search Problems
- Heaps: Min Heap, Max Heap
- Graphs: BFS, DFS, Shortest Path, Minimum Spanning Tree
- Greedy Algorithms: Activity Selection, Interval Scheduling & Merging
- Greedy on Arrays: Jump Game, Gas Station, Candy Distribution
- Greedy on Graphs: Kruskal's & Prim's MST, Dijkstra's Shortest Path
- 1D Dynamic Programming: Fibonacci, Climbing Stairs, House Robber, Coin Change
- DP on Strings: Longest Common Subsequence, Edit Distance, Palindrome Partitioning
- 2D / Grid DP: Unique Paths, Minimum Path Sum, Matrix Chain Multiplication
- Subset & Knapsack DP: 0/1 Knapsack, Subset Sum, Partition Equal Subset
- DP on Trees: Diameter of Binary Tree, Maximum Path Sum, Tree DP patterns
- Interval DP: Burst Balloons, Minimum Cost to Merge Stones
- Bitmask DP: Travelling Salesman Problem, Assignment Problem
Master the architectural patterns required for Senior Engineer (SDE-2/SDE-3) roles at FAANG.
High Level Design (HLD)
- 1. Architecture Patterns:
- Monolithic vs Microservices
- Event-Driven Architecture & Message Queues (Kafka/RabbitMQ)
- 2. Scalability & Performance:
- Horizontal vs Vertical Scaling
- Load Balancing (L4 vs L7) & Consistent Hashing
- Caching Strategies (Redis/Memcached, Write-through vs Write-back)
- 3. Database Design:
- SQL vs NoSQL (Cassandra, MongoDB, DynamoDB)
- Database Sharding, Replication & CAP Theorem
- 4. Real-World Case Studies:
- Design Instagram / Twitter (Social Feed)
- Design Uber / Swiggy (Location Based Services)
- Design WhatsApp (Chat Systems)
Low Level Design (LLD)
- 1. Object-Oriented Design (OOD):
- SOLID Principles (Single Responsibility, Open/Closed, etc.)
- DRY, KISS, and YAGNI Principles
- 2. Design Patterns (Hands-on):
- Creational: Singleton, Factory, Builder
- Structural: Adapter, Decorator, Facade
- Behavioral: Observer, Strategy, Command
- 3. UML & Visualization:
- Class Diagrams & Sequence Diagrams
- Activity & Use Case Diagrams
- 4. Machine Coding Rounds:
- Design a Parking Lot
- Design Splitwise / Expense Sharing App
- Design a Rate Limiter
Mock Interviews
Conducted by professionals working at top tech companies to simulate real pressure.
Resume Reviews
40+ ATS-friendly templates. Optimization for 90+ score visibility to get shortlisted.
LinkedIn Review
Make your profile instantly likeable by hiring managers of top tech companies.
Referrals to Top Tech
Exclusive access to our hiring network for referrals to product-based companies.
Personal 1:1 Mentorship
Weekly 1:1 strategy calls with Pranjal Jain to review your progress, identify gaps, and keep you on track until you're placed.
In 2026, FAANG and product companies added AI to SDE interviews. Even general SDE roles now include a 10–15 min rapid-fire AI segment. AI Engineer roles grew 240% in early 2026. This module is built for software engineers — not data scientists. No math-heavy derivations. Pure interview-focus.
AI Literacy & LLM Fundamentals
- ML Basics for SDEs: Supervised vs unsupervised learning, bias-variance tradeoff, overfitting, train/val/test split, evaluation metrics (precision, recall, F1)
- Transformer Architecture: Attention mechanism (Q, K, V), multi-head attention, positional encoding, encoder-only vs decoder-only (BERT vs GPT)
- Large Language Models: Tokenization (BPE), context windows, temperature & sampling (top-p, top-k), fine-tuning vs prompting, LoRA, hallucination & mitigations
- Embeddings: What they are, sentence embeddings, cosine similarity, dot product — the backbone of semantic search and RAG
RAG & Vector Search
- RAG Architecture: Full indexing and query pipeline — chunking, embedding, vector search, context assembly, LLM generation
- Chunking Strategies: Fixed-size, sentence-based, recursive, semantic — tradeoffs and when to use each
- Vector Databases: Pinecone, Qdrant, Weaviate, Chroma, pgvector, Firestore vector search — choosing the right one
- ANN / HNSW: Why brute-force doesn't scale, how HNSW works, approximate nearest neighbour search
- Hybrid Search: BM25 + vector search combined, re-ranking with cross-encoders, HyDE
- RAG Evaluation: RAGAS framework — faithfulness, relevance, context precision & recall
Prompt Engineering & AI Agents
- Prompting Techniques: Zero-shot, few-shot, chain-of-thought, system prompts, structured output (JSON mode)
- Function Calling / Tool Use: How LLMs call tools — the mechanism behind every AI agent
- AI Agents: The agent loop (perceive → decide → act), memory systems (in-context vs external), tool types
- Agent Patterns: ReAct, Plan-and-Execute, multi-agent supervisor, common failure modes
- Prompt Injection: Attack types and production defences
GenAI System Design
- The 5-Step Framework: Requirements → Indexing Strategy → Query Pipeline → Evaluation → Scaling
- Enterprise RAG Chatbot: Multi-source connectors, access control, incremental indexing, citation validation
- Code Completion System: Fill-in-the-middle, context assembly, speculative decoding, latency budgeting
- Document Q&A: RAG vs full-context decision, PDF parsing, multi-document retrieval
- LLM Serving at Scale: Continuous batching, PagedAttention (vLLM), quantization, KV cache management
- AI Agent System Design: Tool registry, safety layer, error propagation, observability
10 Checkpoints — Read in Order
Capstone Project: "PrepMind" — AI RAG Assistant
Build and deploy a full RAG chatbot on top of your existing Firebase project. Uses Firestore's native vector search (launched 2024), Firebase Genkit, and Gemini API — all on the free tier. This is a deployable, live project you demo in interviews.
I Don't Just Teach, I Ensure You Win
Most students give up due to lack of consistency.
As your personal mentor, I fix that with 3 layers of accountability.
Weekly 1:1 Strategy Calls
We get on a personal call every week to review your progress, identify weak spots, and create a custom roadmap for the next 7 days.
Daily Consistency Updates
I take daily updates personally. If you slack off, I will be there to push you back on track. Consistency is the only hack.
Direct Doubt Support
Don't stay stuck on a problem for hours. I am personally available to clear your doubts so your learning never stops.
Meet Your Mentor
Pranjal Jain
CEO & Co-Founder, Prepflix
"I am not here to just give you lectures. I am here to be your partner, hold you accountable, and make sure you cross the finish line into a top tech company."
Don't learn from just a teacher — learn from someone who's cracked the very interviews you're targeting. Including the new AI rounds.
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