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1

DSA · foundations

4 topics

Complexity thinking and the linear structures everything builds on.

Big-O→

time & space

Arrays←

13 problems

Strings→

10 problems

Hashing & maps←

8 problems

2

DSA · core patterns

6 topics

The patterns interviewers reach for again and again.

Two pointers→

8 problems

Sliding window←

8 problems

Prefix sum→

6 problems

Binary search←

10 problems

Sorting algorithms→

5 problems

Recursion & backtracking←

10 problems

3

DSA · data structures & graphs

7 topics

Trees, heaps, tries, and graphs — the onsite bread and butter.

Linked lists→

10 problems

Stacks & queues←

8 problems

Monotonic stack→

6 problems

Trees & BST←

14 problems

Heaps / priority queues→

7 problems

Tries←

4 problems

Graphs→

14 problems

4

DSA · DP & advanced

7 topics

Dynamic programming is the make-or-break topic for ₹30 LPA+.

Greedy→

8 problems

Dynamic programming←

18 problems

Bit manipulation→

6 problems

Math & number theory←

6 problems

Intervals→

5 problems

Matrix / 2D←

5 problems

Segment tree / Fenwick→

4 problems

5

Aptitude · quantitative

13 topics

The online-assessment round that gates most placements. Speed + accuracy.

Number system→

8 questions

Percentages←

8 questions

Ratio & proportion→

8 questions

Averages & ages←

8 questions

Time, speed & distance→

8 questions

Trains, boats & streams←

8 questions

Time & work, pipes→

8 questions

Profit, loss & interest←

8 questions

Mixtures & alligations→

8 questions

Permutations & combinations←

8 questions

Probability→

8 questions

Progressions←

8 questions

Mensuration & geometry→

8 questions

6

Aptitude · reasoning, verbal & DI

10 topics

Logical reasoning, verbal ability, and data interpretation.

Series & analogies→

8 questions

Coding–decoding←

8 questions

Blood relations & directions→

8 questions

Syllogisms & Venn←

8 questions

Seating arrangement & puzzles→

8 questions

Clocks & calendars←

8 questions

Cubes & dice→

8 questions

Reading comprehension←

8 questions

Sentence correction & para-jumbles→

8 questions

Data interpretation←

8 questions

7

Core CS fundamentals

5 topics

What separates "can code" from "understands systems".

Operating systems→

scheduling, deadlocks, memory

DBMS & SQL←

joins, normalization, transactions

Computer networks→

TCP/IP, HTTP, DNS, TLS

OOP + SOLID + design patterns←
Computer architecture→

cache, pipelining

8

System design & LLD

9 topics

Concept clarity and trade-offs — the unlock for higher bands.

Scalability & load balancing→
Caching & CDN←
DB scaling→

sharding, replication

CAP & consistency←
Message queues & rate limiting→
API design←

REST / GraphQL / gRPC

Consistent hashing→
LLD: parking lot, BookMyShow, Splitwise←
Classic designs: URL shortener, chat, feed→
9

Python & math foundation

4 topics

The language and maths the rest stands on.

Python: typing, async, Pydantic v2, uv/poetry→
NumPy & pandas←
Linear algebra, probability, gradients→
Docker, Git, REST/gRPC clients←
10

Classical ML

4 topics

Don’t skip it, even for GenAI.

Supervised & unsupervised learning→
scikit-learn; XGBoost / LightGBM←

tabular default

Evaluation: precision/recall/F1, ROC-AUC→
Feature engineering; bias–variance←
11

Deep learning & the Transformer

4 topics

Understand attention, not just call it.

PyTorch: autograd, training loops, AdamW
The Transformer: attention, positional encoding, KV cache
Tokenization

BPE/SentencePiece

Hugging Face transformers / datasets
12

LLM applications & prompting

4 topics

The core of the modern role.

LLM APIs: OpenAI, Claude, Gemini, open-weights
Context windows, temperature, function calling, JSON mode
Prompt engineering as engineering

few-shot, CoT, templating

Streaming (SSE), cost/latency routing, token accounting
13

RAG & vector search

4 topics

Ground models in real data.

Embeddings

text-embedding-3, BGE, Nomic

Vector DBs: pgvector, Pinecone, Qdrant, Chroma
Chunking, hybrid search (BM25 + dense), reranking
Advanced RAG: query rewriting, HyDE; RAGAS evals
14

Agents & orchestration

4 topics

Reason → tool → observe → repeat.

Agent loop + memory

short/long/working

LangGraph (production), LlamaIndex, CrewAI
Model Context Protocol (MCP) — the 2025 tool standard
Guardrails & human-in-the-loop
15

Fine-tuning & LLMOps

4 topics

Customise, serve, and measure quality.

RAG vs fine-tune vs prompt — the decision itself
LoRA / QLoRA, DPO/ORPO, quantization

GGUF/AWQ

Serving: vLLM, TGI; batching, KV-cache
Evals & tracing: LangSmith, Langfuse, W&B; drift monitoring
16

Build proof of work

4 topics

Projects that signal seniority — deployed, tested, not tutorials.

Ship 2–3 real projects, deployed with monitoring→
Clean READMEs + pinned repos←
A merged open-source PR→

issue → PR → review

A written design doc with trade-offs + load-test numbers←
17

Package your profile

4 topics

A non-brand-name profile has to make the signal obvious.

Single-column ATS résumé, keywords mirrored from the JD→
Quantified bullets — impact, not tasks←
Optimise LinkedIn headline & keywords→
Polish GitHub — pinned repos, clean READMEs←
18

Sharpen for interviews

4 topics

Reps convert — DSA rounds, core CS, project deep-dive, design.

Stay warm on DSA through the interview window→
Project deep-dive: defend every résumé line for 20 min←
LLD out loud: parking lot, rate limiter, URL shortener→
HR round: 5–6 STAR stories + company research←
19

Apply & get the offer

4 topics

Off-campus, discovery is your job — and leverage beats one interview.

Message college alumni at target companies→

referrals convert best

Apply within 24–48h — Naukri, LinkedIn, company pages←
Enter hackathons/contests (Flipkart GRiD, Amazon ML) — direct funnels→
Reach a higher band with a real competing offer, then negotiate←
Roadmaps

AI / ML Engineer

The role has split: the AI Engineer (RAG, agents, LLMOps) is ~80% of hiring, the classical ML Engineer is the training/modeling path. This covers both, weighted toward where the demand is — GenAI.

Explore companiesTrack applications

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0/105 skills
  1. Arrays · 13 problemsStrings · 10 problemsHashing & maps · 8 problems

    Learn withPractice DSA ›DSA notes ›

  2. Two pointers · 8 problemsSliding window · 8 problemsPrefix sum · 6 problemsBinary search · 10 problemsSorting algorithms · 5 problemsRecursion & backtracking · 10 problems

    Learn withPractice DSA ›DSA notes ›

  3. Linked lists · 10 problemsStacks & queues · 8 problemsMonotonic stack · 6 problemsTrees & BST · 14 problemsHeaps / priority queues · 7 problemsTries · 4 problemsGraphs · 14 problems

    Learn withPractice DSA ›DSA notes ›

  4. Greedy · 8 problemsDynamic programming · 18 problemsBit manipulation · 6 problemsMath & number theory · 6 problemsIntervals · 5 problemsMatrix / 2D · 5 problemsSegment tree / Fenwick · 4 problems

    Learn withPractice DSA ›DSA notes ›

  5. Number system · 8 questionsPercentages · 8 questionsRatio & proportion · 8 questionsAverages & ages · 8 questionsTime, speed & distance · 8 questionsTrains, boats & streams · 8 questionsTime & work, pipes · 8 questionsProfit, loss & interest · 8 questionsMixtures & alligations · 8 questionsPermutations & combinations · 8 questionsProbability · 8 questionsProgressions · 8 questionsMensuration & geometry · 8 questions

    Learn withPractice aptitude ›

  6. Series & analogies · 8 questionsCoding–decoding · 8 questionsBlood relations & directions · 8 questionsSyllogisms & Venn · 8 questionsSeating arrangement & puzzles · 8 questionsClocks & calendars · 8 questionsCubes & dice · 8 questionsReading comprehension · 8 questionsSentence correction & para-jumbles · 8 questionsData interpretation · 8 questions

    Learn withPractice aptitude ›

  7. Learn withCS Fundamentals ›

  8. Learn withRoadmaps ›Cheatsheets ›

  9. Learn withData Science track ›

  10. Learn withData Scientist Roadmap ›

  11. Learn withLabs & IDE ›Curated Repos ›

  12. Learn withResume Builder ›LinkedIn Enhancer ›

  13. Learn withRoadmaps ›Practice ›

  14. Learn withJob Board ›Application Tracker ›Companies ›

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