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Sde

SDE + AI Engineer Goal: Become interview-ready and production-capable for SDE, Backend Engineer, AI Engineer, GenAI Engineer, and AI/Backend Engineer fresher roles.

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    llms.txt

SDE + AI Engineer

Goal: Become interview-ready and production-capable for SDE, Backend Engineer, AI Engineer, GenAI Engineer, and AI/Backend Engineer fresher roles.

0. Mastery Rules

For every important concept, reach these levels:

  • L1 — Understand: I can explain what it is.

  • L2 — Implement: I can code/use it without following a tutorial.

  • L3 — Internals: I understand what happens underneath.

  • L4 — Interview: I can answer follow-up questions and solve problems involving it.

  • L5 — Production: I understand its trade-offs, failure modes, scaling and security implications.

Completion rule

A major topic is DONE only when I can:

Explain → Implement → Debug → Optimize → Discuss Trade-offs

1. Programming Fundamentals

1.1 C++ / General Programming

  • Variables and data types

  • Stack vs heap

  • Pointers

  • References

  • Memory allocation

  • Arrays

  • Strings

  • Functions

  • Recursion

  • Scope

  • Lifetime

  • Pass by value

  • Pass by reference

  • Const correctness

  • Error handling

  • Time complexity

  • Space complexity

1.2 C++

  • STL overview

  • vector

  • string

  • pair

  • tuple

  • stack

  • queue

  • deque

  • priority_queue

  • set

  • unordered_set

  • map

  • unordered_map

  • Iterators

  • Algorithms

  • Lambda functions

  • References

  • Move semantics

  • Smart pointers

  • RAII

  • Rule of 3

  • Rule of 5

  • Rule of 0

1.3 Python

  • Python syntax

  • List / tuple / set / dict

  • Comprehensions

  • Functions

  • *args / **kwargs

  • Lambda

  • Iterators

  • Generators

  • Decorators

  • Context managers

  • Exceptions

  • Modules

  • Packages

  • Virtual environments

  • Type hints

  • Dataclasses

  • Pydantic

  • Python memory model

  • Garbage collection

  • GIL

  • multiprocessing

  • threading

  • asyncio

2. Object-Oriented Programming

Core

  • Class

  • Object

  • Constructor

  • Destructor

  • Encapsulation

  • Abstraction

  • Inheritance

  • Polymorphism

Deeper OOP

  • Function overloading

  • Function overriding

  • Virtual functions

  • Pure virtual functions

  • Abstract classes

  • Interfaces

  • Static members

  • Friend functions

  • Multiple inheritance

  • Diamond problem

  • Composition

  • Aggregation

  • Association

  • Dependency

Design Principles

  • SOLID

  • Single Responsibility

  • Open/Closed

  • Liskov Substitution

  • Interface Segregation

  • Dependency Inversion

  • Composition over inheritance

Design Patterns

  • Singleton

  • Factory

  • Abstract Factory

  • Builder

  • Strategy

  • Observer

  • Adapter

  • Decorator

  • Repository

  • Dependency Injection

3. Data Structures & Algorithms

Highest-priority SDE interview section.

3.1 Complexity

  • Big-O

  • Big-Theta

  • Big-Omega

  • Amortized analysis

  • Best / average / worst case

  • Recurrence relations

  • Master theorem

3.2 Arrays

  • Traversal

  • Insertion / deletion

  • Prefix sum

  • Difference array

  • Frequency counting

  • Kadane’s algorithm

  • Sorting

  • In-place algorithms

  • Matrix traversal

  • Rotations

Patterns

  • Two pointers

  • Sliding window

  • Prefix sum

  • Hashing

  • Binary search

Problems

  • Two Sum

  • Best Time to Buy/Sell Stock

  • Maximum Subarray

  • Product Except Self

  • 3Sum

  • Container With Most Water

  • Subarray Sum Equals K

  • Merge Intervals

  • Insert Interval

3.3 Strings

  • Character frequency

  • Anagrams

  • Palindromes

  • Substrings

  • String hashing

  • String manipulation

  • Sliding window on strings

  • Pattern matching

Algorithms

  • KMP

  • Rabin-Karp

  • Z algorithm

3.4 Linked Lists

  • Singly linked list

  • Doubly linked list

  • Circular linked list

  • Reverse linked list

  • Fast/slow pointers

  • Cycle detection

  • Merge lists

  • Intersection

  • Reordering

  • LRU cache implementation

Problems

  • Reverse Linked List

  • Merge Two Sorted Lists

  • Linked List Cycle

  • Remove Nth Node

  • Reorder List

  • Merge K Sorted Lists

3.5 Stack & Queue

  • Stack

  • Queue

  • Deque

  • Monotonic stack

  • Monotonic queue

  • Min stack

  • Expression evaluation

  • Infix / postfix / prefix

Problems

  • Valid Parentheses

  • Min Stack

  • Daily Temperatures

  • Largest Rectangle in Histogram

  • Sliding Window Maximum

3.7 Trees

  • Binary tree

  • Tree terminology

  • DFS

  • BFS

  • Preorder

  • Inorder

  • Postorder

  • Level order

  • Height / depth

  • Diameter

  • Balanced tree

  • Path problems

  • Lowest Common Ancestor

BST

  • BST properties

  • Search

  • Insert

  • Delete

  • Validation

  • Successor / predecessor

Advanced Trees

  • Heap

  • Min heap

  • Max heap

  • Trie

  • Segment tree

  • Fenwick tree

Problems

  • Maximum Depth

  • Invert Binary Tree

  • Diameter

  • Level Order Traversal

  • Validate BST

  • Kth Smallest BST

  • Lowest Common Ancestor

  • Serialize / Deserialize

3.8 Graphs

  • Graph terminology

  • Adjacency matrix

  • Adjacency list

  • BFS

  • DFS

  • Visited arrays

  • Connected components

  • Cycle detection

  • Bipartite graphs

  • Topological sorting

  • DAG

  • Shortest path

  • Minimum spanning tree

Algorithms

  • BFS shortest path

  • Dijkstra

  • Bellman-Ford

  • Floyd-Warshall

  • Kruskal

  • Prim

  • Union-Find / DSU

  • Kahn’s algorithm

Problems

  • Number of Islands

  • Clone Graph

  • Course Schedule

  • Rotting Oranges

  • Pacific Atlantic Water Flow

  • Network Delay Time

  • Graph Valid Tree

3.9 Greedy

  • Greedy principle

  • Activity selection

  • Interval scheduling

  • Fractional knapsack

  • Jump Game

  • Gas Station

  • Huffman coding

3.10 Dynamic Programming

Foundations

  • Recursion

  • Overlapping subproblems

  • Optimal substructure

  • Memoization

  • Tabulation

  • Space optimization

Patterns

  • 1D DP

  • 2D DP

  • Grid DP

  • Knapsack

  • Subset sum

  • Partition

  • LCS

  • LIS

  • Interval DP

  • State-machine DP

Problems

  • Climbing Stairs

  • House Robber

  • Coin Change

  • Word Break

  • Longest Increasing Subsequence

  • Longest Common Subsequence

  • 0/1 Knapsack

  • Partition Equal Subset Sum

4. DBMS

Fundamentals

  • Database vs DBMS

  • Relational databases

  • Tables

  • Rows

  • Columns

  • Primary keys

  • Foreign keys

  • Candidate keys

  • Composite keys

  • Constraints

Normalization

  • Functional dependency

  • 1NF

  • 2NF

  • 3NF

  • BCNF

  • Denormalization

  • Normalization trade-offs

Transactions

  • Transaction

  • ACID

  • Atomicity

  • Consistency

  • Isolation

  • Durability

Concurrency

  • Dirty read

  • Non-repeatable read

  • Phantom read

  • Lost update

  • Isolation levels

  • Locks

  • Shared lock

  • Exclusive lock

  • MVCC

  • Deadlocks

Indexing

  • What is an index?

  • Why indexes improve reads

  • B-Tree

  • B+ Tree

  • Hash indexes

  • Composite indexes

  • Covering indexes

  • Index selectivity

  • Query planner

  • EXPLAIN

  • When indexes hurt performance

Advanced

  • Replication

  • Sharding

  • Partitioning

  • Read replicas

  • Connection pooling

  • CAP theorem

  • Eventual consistency

5. SQL

Basics

  • SELECT

  • WHERE

  • ORDER BY

  • GROUP BY

  • HAVING

  • DISTINCT

  • LIMIT

  • CASE

  • NULL handling

  • COALESCE

Joins

  • INNER JOIN

  • LEFT JOIN

  • RIGHT JOIN

  • FULL JOIN

  • CROSS JOIN

  • SELF JOIN

Advanced SQL

  • Subqueries

  • Correlated subqueries

  • CTE

  • Recursive CTE

  • Window functions

  • ROW_NUMBER

  • RANK

  • DENSE_RANK

  • LEAD

  • LAG

  • PARTITION BY

  • Running totals

  • Conditional aggregation

Interview Problems

  • Second highest salary

  • Nth highest salary

  • Duplicate records

  • Employees above average

  • Highest salary per department

  • Second highest salary per department

  • Top N per group

  • Consecutive records

  • Running total

  • Customers with no orders

  • Employee-manager relationship

  • Duplicate removal

  • Gaps and islands

6. Operating Systems

Processes & Threads

  • Program vs process

  • Process states

  • PCB

  • Context switching

  • Process scheduling

  • Threads

  • User vs kernel threads

  • Process vs thread

  • Concurrency vs parallelism

Scheduling

  • FCFS

  • SJF

  • SRTF

  • Round Robin

  • Priority scheduling

  • Multilevel queues

Synchronization

  • Race condition

  • Critical section

  • Mutex

  • Semaphore

  • Monitor

  • Producer-consumer

  • Reader-writer

  • Dining philosophers

Deadlocks

  • Deadlock

  • Four necessary conditions

  • Deadlock prevention

  • Deadlock avoidance

  • Banker’s algorithm

  • Deadlock detection

Memory

  • Stack

  • Heap

  • Virtual memory

  • Paging

  • Segmentation

  • Page table

  • TLB

  • Page fault

  • Demand paging

  • Page replacement

  • FIFO

  • LRU

  • Optimal replacement

File Systems

  • Files

  • Directories

  • Inodes

  • File descriptors

  • Permissions

  • System calls

7. Computer Networks

Fundamentals

  • OSI model

  • TCP/IP model

  • MAC address

  • IP address

  • IPv4

  • IPv6

  • Subnetting

  • Ports

  • Sockets

Protocols

  • HTTP

  • HTTPS

  • TCP

  • UDP

  • DNS

  • DHCP

  • ARP

  • ICMP

TCP

  • Three-way handshake

  • Four-way termination

  • Sequence numbers

  • ACK

  • Retransmission

  • Flow control

  • Congestion control

  • Slow start

Web

  • HTTP methods

  • HTTP status codes

  • Headers

  • Cookies

  • Sessions

  • Caching

  • CORS

  • WebSockets

  • SSE

Security

  • TLS

  • Certificates

  • Encryption

  • Symmetric encryption

  • Asymmetric encryption

  • Hashing

Must Explain

  • What happens when entering a URL?

  • How DNS works

  • TCP vs UDP

  • HTTP vs HTTPS

  • TLS handshake

  • How WebSockets work

8. Backend Engineering

HTTP/API

  • REST

  • REST principles

  • HTTP methods

  • Idempotency

  • Status codes

  • Headers

  • Query parameters

  • Path parameters

  • Request body

  • Pagination

  • Filtering

  • Sorting

  • API versioning

Authentication

  • Authentication vs authorization

  • Sessions

  • Cookies

  • JWT

  • Access tokens

  • Refresh tokens

  • OAuth2

  • RBAC

  • Password hashing

API Security

  • Input validation

  • SQL injection

  • XSS

  • CSRF

  • CORS

  • Rate limiting

  • Brute-force protection

  • Secrets management

  • TLS

Architecture

  • MVC

  • Layered architecture

  • Service layer

  • Repository pattern

  • Dependency injection

  • Clean architecture

  • SOLID in backend

9. FastAPI

  • Application setup

  • Routing

  • APIRouter

  • Path parameters

  • Query parameters

  • Request bodies

  • Pydantic

  • Response models

  • Validation

  • Dependency injection

  • Middleware

  • Exception handling

  • Authentication

  • JWT

  • OAuth2

  • Background tasks

  • Async endpoints

  • Streaming responses

  • WebSockets

  • OpenAPI

  • Swagger

  • Testing

  • Project structure

  • Production deployment

Async Python

  • Event loop

  • Coroutine

  • await

  • async

  • Tasks

  • Concurrent I/O

  • Blocking vs non-blocking

  • Async DB drivers

  • Thread pool

  • When NOT to use async

10. Django / Django REST Framework

  • Django architecture

  • Models

  • Views

  • URLs

  • Templates

  • ORM

  • QuerySets

  • Migrations

  • Serializers

  • ViewSets

  • Routers

  • Authentication

  • Permissions

  • Middleware

  • Signals

  • Pagination

  • Filtering

  • Caching

  • Transactions

  • Testing

  • Production deployment

11. PostgreSQL

  • Tables

  • Constraints

  • Relationships

  • Joins

  • Transactions

  • Indexes

  • EXPLAIN

  • EXPLAIN ANALYZE

  • Query optimization

  • CTEs

  • Window functions

  • JSON/JSONB

  • Full-text search

  • Extensions

  • Connection pooling

  • Replication

  • Backup / restore

12. Redis

  • What Redis is

  • In-memory architecture

  • Key-value model

  • Strings

  • Lists

  • Sets

  • Sorted sets

  • Hashes

  • TTL

  • Cache-aside

  • Write-through cache

  • Write-behind cache

  • Cache invalidation

  • Distributed locks

  • Rate limiting

  • Pub/Sub

  • Streams

13. Message Queues & Background Jobs

  • Why queues exist

  • Producer

  • Consumer

  • Message broker

  • RabbitMQ

  • Kafka fundamentals

  • Celery

  • Task queues

  • Retries

  • Dead-letter queues

  • Idempotency

  • At-least-once delivery

  • At-most-once delivery

  • Exactly-once semantics

  • Async processing

14. Docker & DevOps

Docker

  • Containers

  • Images

  • Dockerfile

  • Layers

  • Volumes

  • Networks

  • Environment variables

  • Docker Compose

  • Multi-stage builds

  • Container security

Linux

  • Processes

  • Signals

  • Permissions

  • File system

  • Environment variables

  • SSH

  • grep

  • awk

  • sed

  • curl

  • systemctl

  • journalctl

  • networking commands

CI/CD

  • GitHub Actions

  • Build pipeline

  • Tests

  • Linting

  • Docker build

  • Deployment

  • Secrets

  • Rollbacks

15. Git

  • init

  • clone

  • add

  • commit

  • push

  • pull

  • branch

  • merge

  • rebase

  • cherry-pick

  • stash

  • reset

  • revert

  • reflog

  • merge conflicts

  • .gitignore

  • GitHub PR workflow

16. System Design

Fundamentals

  • Scalability

  • Availability

  • Reliability

  • Latency

  • Throughput

  • Fault tolerance

  • Horizontal scaling

  • Vertical scaling

  • Stateless services

  • Load balancing

Components

  • Load balancer

  • Reverse proxy

  • API gateway

  • Cache

  • Database

  • Read replica

  • Message queue

  • Object storage

  • CDN

  • Search engine

Distributed Systems

  • CAP theorem

  • Consistency

  • Availability

  • Partition tolerance

  • Strong consistency

  • Eventual consistency

  • Replication

  • Sharding

  • Leader/follower

  • Consensus basics

Reliability

  • Retry

  • Exponential backoff

  • Timeout

  • Circuit breaker

  • Rate limiter

  • Bulkhead

  • Idempotency

  • Graceful degradation

Designs

  • URL Shortener

  • Rate Limiter

  • Chat Application

  • Notification System

  • File Storage

  • Instagram Feed

  • YouTube-like system

  • Ride-sharing system

  • Job Queue

17. Machine Learning Fundamentals

Math

  • Vectors

  • Matrices

  • Matrix multiplication

  • Dot product

  • Eigenvalues

  • Eigenvectors

  • Probability

  • Conditional probability

  • Bayes theorem

  • Mean

  • Variance

  • Standard deviation

  • Distributions

  • Derivatives

  • Partial derivatives

  • Gradients

ML Concepts

  • Supervised learning

  • Unsupervised learning

  • Semi-supervised learning

  • Regression

  • Classification

  • Clustering

  • Feature engineering

  • Training

  • Validation

  • Testing

Algorithms

  • Linear regression

  • Logistic regression

  • KNN

  • Naive Bayes

  • Decision trees

  • Random forest

  • Gradient boosting

  • XGBoost

  • SVM

  • K-Means

  • PCA

Model Problems

  • Overfitting

  • Underfitting

  • Bias

  • Variance

  • Regularization

  • Data leakage

  • Class imbalance

  • Feature scaling

  • Cross-validation

Metrics

  • Accuracy

  • Precision

  • Recall

  • F1

  • Confusion matrix

  • ROC-AUC

  • PR-AUC

  • MAE

  • MSE

  • RMSE

  • R²

18. Deep Learning

Neural Networks

  • Neuron

  • Weights

  • Bias

  • Activation functions

  • Forward propagation

  • Loss function

  • Backpropagation

  • Gradient descent

Activations

  • Sigmoid

  • Tanh

  • ReLU

  • Leaky ReLU

  • Softmax

  • GELU

Training

  • Batch

  • Epoch

  • Learning rate

  • Batch size

  • Optimizer

  • SGD

  • Momentum

  • Adam

  • AdamW

  • Weight decay

  • Learning rate scheduling

  • Early stopping

Architectures

  • CNN

  • RNN

  • LSTM

  • GRU

  • Autoencoder

  • GAN

  • Diffusion models

19. Transformers

Critical AI Engineer topic.

  • Tokenization

  • Vocabulary

  • Token IDs

  • Embeddings

  • Positional encoding

  • Self-attention

  • Query

  • Key

  • Value

  • Attention scores

  • Scaled dot-product attention

  • Multi-head attention

  • Feed-forward network

  • Residual connections

  • Layer normalization

  • Transformer encoder

  • Transformer decoder

  • Encoder-decoder architecture

  • Causal masking

  • Cross-attention

Must understand mathematically

Attention(Q,K,V)
=
softmax(QKᵀ / √dₖ)V

  • Explain Q

  • Explain K

  • Explain V

  • Explain scaling

  • Explain softmax

  • Explain masking

  • Explain multi-head attention

20. NLP

  • Text preprocessing

  • Tokenization

  • Stemming

  • Lemmatization

  • Word embeddings

  • Word2Vec

  • GloVe

  • Contextual embeddings

  • Sequence modeling

  • Attention

  • Transformers

21. Large Language Models

Fundamentals

  • LLM architecture

  • Parameters

  • Weights

  • Tokens

  • Context window

  • Vocabulary

  • Logits

  • Probability distribution

  • Next-token prediction

Training

  • Pretraining

  • Dataset construction

  • Data cleaning

  • Tokenization

  • Pretraining objective

  • Instruction tuning

  • Fine-tuning

  • RLHF

  • Preference optimization

Inference

  • Temperature

  • Top-K

  • Top-P

  • Greedy decoding

  • Beam search

  • Sampling

  • KV cache

  • Batching

  • Streaming

LLM Problems

  • Hallucination

  • Context limitations

  • Bias

  • Prompt injection

  • Jailbreaking

  • Data leakage

  • Cost

  • Latency

22. Embeddings

  • What embeddings represent

  • Sentence embeddings

  • Document embeddings

  • Query embeddings

  • Embedding dimensions

  • Semantic similarity

  • Cosine similarity

  • Euclidean distance

  • Dot product

  • Normalization

  • Embedding model selection

  • Multilingual embeddings

23. Vector Databases

  • Why vector databases exist

  • Vector storage

  • Similarity search

  • Approximate nearest neighbor

  • Exact nearest neighbor

  • HNSW

  • IVF

  • Product quantization

  • Metadata filtering

  • Hybrid search

  • Index configuration

  • Recall vs latency trade-off

Learn at least one deeply:

  • pgvector

  • Qdrant

  • Weaviate

  • Pinecone

  • Milvus

24. RAG

Primary specialization.

Basic RAG

  • Document ingestion

  • Document parsing

  • Cleaning

  • Chunking

  • Embedding

  • Vector storage

  • Retrieval

  • Context construction

  • Prompt construction

  • Generation

Chunking

  • Fixed-size chunking

  • Recursive chunking

  • Sentence chunking

  • Semantic chunking

  • Document-aware chunking

  • Chunk overlap

  • Chunk-size trade-offs

Retrieval

  • Dense retrieval

  • Sparse retrieval

  • BM25

  • Hybrid retrieval

  • Top-K

  • Metadata filtering

  • Similarity thresholds

Advanced RAG

  • Query rewriting

  • Query expansion

  • Multi-query retrieval

  • HyDE

  • Parent-child retrieval

  • Contextual retrieval

  • Reranking

  • Cross-encoder reranker

  • Corrective RAG

  • Agentic RAG

  • Graph RAG

RAG Failure Modes

  • Bad chunking

  • Wrong retrieval

  • Missing context

  • Too much context

  • Irrelevant context

  • Lost-in-the-middle

  • Hallucination

  • Stale documents

  • Duplicate documents

25. RAG Evaluation

Must know for serious AI engineering.

Retrieval Metrics

  • Recall@K

  • Precision@K

  • MRR

  • NDCG

  • Hit rate

Generation Metrics

  • Faithfulness

  • Answer relevance

  • Context relevance

  • Groundedness

  • Citation correctness

Evaluation System

  • Create evaluation dataset

  • Define ground truth

  • Offline evaluation

  • Online evaluation

  • Regression testing

  • A/B testing

  • Human evaluation

  • LLM-as-judge

  • Error analysis

26. Prompt Engineering

  • System prompts

  • User prompts

  • Role prompting

  • Few-shot prompting

  • Zero-shot prompting

  • Chain-of-thought concepts

  • Structured outputs

  • JSON outputs

  • Prompt templates

  • Prompt versioning

  • Prompt injection defense

  • Context management

27. AI Agents

Fundamentals

  • Agent definition

  • Agent vs workflow

  • Planning

  • Reasoning

  • Tool use

  • Memory

  • Observation

  • Action

  • Agent loop

Tool Calling

  • Function calling

  • Tool schemas

  • Tool selection

  • Tool execution

  • Tool validation

  • Tool errors

  • Retries

  • Timeouts

Advanced

  • ReAct

  • Planning agents

  • Reflection

  • Multi-agent systems

  • Human-in-the-loop

  • Long-term memory

  • Agent evaluation

  • Agent security

28. AI Frameworks

Learn concepts first; frameworks second.

Hugging Face

  • Transformers

  • Tokenizers

  • Model loading

  • Pipelines

  • Datasets

  • Model Hub

LangChain

  • Models

  • Prompts

  • Retrievers

  • Chains

  • Tools

  • Agents

  • Memory

  • LCEL concepts

LlamaIndex

  • Document ingestion

  • Nodes

  • Indexes

  • Retrievers

  • Query engines

  • Agents

Local LLMs

  • Ollama

  • Model formats

  • Quantization basics

  • Local inference

  • GPU vs CPU inference

29. AI Model Optimization

  • Quantization

  • INT8

  • INT4

  • FP16

  • BF16

  • KV cache

  • Batching

  • Continuous batching

  • Speculative decoding

  • Distillation

  • Pruning

  • LoRA

  • QLoRA

  • PEFT

30. Fine-Tuning

  • Fine-tuning vs RAG

  • When to fine-tune

  • Dataset preparation

  • Instruction dataset

  • Training/validation split

  • LoRA

  • QLoRA

  • PEFT

  • Hyperparameters

  • Evaluation

  • Catastrophic forgetting

  • Overfitting

31. LLM Serving

  • Model loading

  • Inference

  • GPU memory

  • VRAM requirements

  • Batching

  • Streaming

  • Token throughput

  • Time to first token

  • Tokens/sec

  • Latency

  • Concurrency

Tools

  • vLLM

  • Hugging Face TGI concepts

  • Ollama

  • ONNX Runtime

  • TensorRT concepts

32. AI + Backend ArchitectureProgress

text SDE CORE [ ] 0% DSA [ ] 0% DBMS/SQL [ ] 0% OS [ ] 0% CN [ ] 0% BACKEND [ ] 0% SYSTEM DESIGN [ ] 0%

ML [ ] 0% DEEP LEARNING [ ] 0% TRANSFORMERS [ ] 0% LLM [ ] 0% RAG [ ] 0% AI AGENTS [ ] 0% AI ENGINEERING [ ] 0%

PROJECTS [ ] 0% INTERVIEWS [ ] 0%

Target

SDE / Backend / AI Engineer / GenAI Engineer 

Be able to design:

Client
   ↓
API Gateway
   ↓
FastAPI
   ↓
Authentication
   ↓
Service Layer
   ├── PostgreSQL
   ├── Redis
   ├── Celery
   └── AI Service
          ├── Embedding Model
          ├── Vector DB
          ├── Reranker
          └── LLM

Understand:

  • Request flow

  • Async processing

  • Streaming

  • Caching

  • Rate limiting

  • Authentication

  • Authorization

  • Observability Progress

text SDE CORE [ ] 0% DSA [ ] 0% DBMS/SQL [ ] 0% OS [ ] 0% CN [ ] 0% BACKEND [ ] 0% SYSTEM DESIGN [ ] 0%

ML [ ] 0% DEEP LEARNING [ ] 0% TRANSFORMERS [ ] 0% LLM [ ] 0% RAG [ ] 0% AI AGENTS [ ] 0% AI ENGINEERING [ ] 0%

PROJECTS [ ] 0% INTERVIEWS [ ] 0%

Target

SDE / Backend / AI Engineer / GenAI Engineer 

  • Failure recovery

  • Cost optimization

  • Latency optimization

  • Horizontal scaling

33. AI Security

  • Prompt injection

  • Indirect prompt injection

  • Jailbreaking

  • Data exfiltration

  • Sensitive data leakage

  • RAG poisoning

  • Malicious documents

  • Unsafe tool calls

  • Excessive agent permissions

  • Output validation

  • Input validation

  • Sandboxing

  • Rate limiting

  • Authentication

  • Authorization

  • Secret management

34. AI Observability

  • Logging

  • Metrics

  • Tracing

  • Request IDs

  • Token usage

  • Latency tracking

  • Cost tracking

  • Retrieval debugging

  • Prompt tracing

  • Model monitoring

  • Error monitoring

Know what to measure:

TTFT
Tokens/sec
Total latency
Prompt tokens
Completion tokens
Cost/request
Retrieval latency
Retrieval quality
Answer quality
Error rate

35. Testing

General

  • Unit testing

  • Integration testing

  • End-to-end testing

  • Mocking

  • Fixtures

  • Test coverage

Backend

  • API tests

  • Authentication tests

  • Database tests

  • Failure tests

  • Load testing

AI

  • Prompt tests

  • Retrieval tests

  • RAG evaluation

  • Regression tests

  • Hallucination tests

  • Safety tests

  • Adversarial tests

36. Cloud

Fundamentals

  • Compute

  • Storage

  • Networking

  • IAM

  • Databases

  • Containers

  • Serverless

  • Load balancing

  • Monitoring

AWS

  • EC2

  • S3

  • RDS

  • Lambda

  • VPC

  • IAM

  • CloudWatch

  • ECS/EKS concepts

37. Projects

Project 1 — Production Backend

Volunteer / Management Platform

  • Authentication

  • Authorization

  • RBAC

  • REST APIs

  • PostgreSQL

  • Redis

  • Background jobs

  • Pagination

  • Filtering

  • Validation

  • Logging

  • Error handling

  • Tests

  • Docker

  • CI/CD

  • Deployment

  • Documentation

  • Architecture diagram

Project 2 — Enterprise RAG

Production Knowledge Assistant

  • Document upload

  • Document parsing

  • Chunking

  • Embeddings

  • Vector DB

  • Hybrid retrieval

  • Reranking

  • LLM

  • Streaming

  • Citations

  • Authentication

  • Document-level permissions

  • Conversation history

  • Redis caching

  • Evaluation dataset

  • RAG metrics

  • Prompt injection protection

  • Observability

  • Docker

  • Deployment

Project 3 — Agentic AI

AI Research / Automation Agent

  • Planner

  • LLM

  • Tool calling

  • Search

  • RAG

  • Memory

  • Multi-step execution

  • Retry handling

  • Human approval

  • Structured output

  • Citation generation

  • Evaluation

  • Security

  • FastAPI

  • Redis

  • Celery

  • PostgreSQL

  • Docker

  • Deployment

38. Interview Preparation

DSA

  • 100 easy problems

  • 100 medium problems

  • 20 hard problems

  • Blind 75

  • NeetCode-style pattern revision

  • Timed contests

  • Re-solve failed problems

SQL

  • 50 basic queries

  • 50 intermediate queries

  • 30 advanced queries

  • Window functions

  • CTE

  • Complex joins

  • Gaps & islands

CS Fundamentals

  • 50 OOP questions

  • 50 DBMS questions

  • 50 OS questions

  • 50 CN questions

Backend

  • REST

  • Authentication

  • JWT

  • Django

  • FastAPI

  • PostgreSQL

  • Redis

  • Celery

  • Docker

  • API design

AI

  • 50 ML questions

  • 50 DL questions

  • 50 LLM questions

  • 50 RAG questions

  • 30 AI system-design questions

  • 20 AI debugging scenarios

39. Project Interview Preparation

For every project, be able to answer:

  • What problem does it solve?

  • Why did you build it?

  • Why these technologies?

  • Explain the architecture.

  • Explain the database schema.

  • Explain the API flow.

  • Explain authentication.

  • Explain authorization.

  • Biggest technical challenge?

  • Biggest bug?

  • Biggest performance bottleneck?

  • How did you debug it?

  • What would you change?

  • How would you scale it?

  • How would you secure it?

  • What happens if a service fails?

  • What happens under 10× traffic?

  • What happens under 100× traffic?

For AI projects

  • Why RAG?

  • Why not fine-tuning?

  • Why this embedding model?

  • Why this vector DB?

  • Why this chunk size?

  • How is retrieval performed?

  • Why Top-K?

  • Why reranking?

  • How did you evaluate it?

  • How did you reduce hallucination?

  • How did you handle prompt injection?

  • How did you reduce latency?

  • How did you reduce cost?

  • How would you scale inference?

40. System Design Practice

Design these from scratch:

  • URL Shortener

  • Rate Limiter

  • Pastebin

  • Chat Application

  • Notification Service

  • File Storage

  • Video Streaming

  • Social Media Feed

  • Search System

  • Ride Sharing

  • Food Delivery

  • Job Queue

  • Distributed Cache

  • AI Chatbot

  • RAG System

  • AI Agent Platform

  • LLM Gateway

For every design:

Requirements
     ↓
API design
     ↓
Data model
     ↓
Architecture
     ↓
Database
     ↓
Caching
     ↓
Scaling
     ↓
Failure handling
     ↓
Security
     ↓
Monitoring
     ↓
Trade-offs

41. Final Mastery Checklist

SDE

  • DSA

  • OOP

  • DBMS

  • SQL

  • OS

  • CN

  • Backend

  • APIs

  • Authentication

  • PostgreSQL

  • Redis

  • Queues

  • Docker

  • Git

  • System Design

  • Testing

  • Security

AI

  • Mathematics

  • ML

  • Deep Learning

  • NLP

  • Transformers

  • LLMs

  • Embeddings

  • Vector DB

  • RAG

  • RAG Evaluation

  • Prompt Engineering

  • Agents

  • Tool Calling

  • Fine-tuning

  • LoRA / QLoRA

  • Quantization

  • LLM Serving

  • AI Security

  • AI Observability

  • AI System Design

Engineering

  • Linux

  • Docker

  • CI/CD

  • Cloud

  • Monitoring

  • Logging

  • Testing

  • Distributed Systems

Proof of Skill

  • Production backend project

  • Production RAG project

  • Agentic AI project

  • All projects deployed

  • All projects documented

  • Architecture diagrams

  • GitHub repositories cleaned

  • Strong README

  • Resume updated

  • LinkedIn updated

  • Mock interviews

  • DSA timed practice

  • SQL timed practice

  • CS fundamentals revision

42. The Golden Rule

Don’t chase technologies.

Build this progression:

                    FUNDAMENTALS
                         │
          ┌──────────────┴──────────────┐
          │                             │
         SDE                            AI
          │                             │
    DSA / DBMS / OS               ML / DL / NLP
    CN / OOP / SQL                     │
          │                       Transformers
          │                             │
       Backend                         LLM
          │                             │
    APIs / DB / Redis                  RAG
          │                             │
    Docker / Cloud                    Agents
          │                             │
          └──────────────┬──────────────┘
                         │
                  SYSTEM DESIGN
                         │
                  PRODUCTION
                         │
                    INTERVIEWS
                         │
                         ▼
               AI-BACKEND ENGINEER

Definition of “Job Ready”

I am job ready when I can:

  • Solve a medium DSA problem in ~30 minutes.

  • Write complex SQL without assistance.

  • Explain OS/DBMS/CN/OOP fundamentals clearly.

  • Build a REST API from scratch.

  • Design authentication and authorization.

  • Design a PostgreSQL schema.

  • Debug backend failures.

  • Dockerize an application.

  • Deploy an application.

  • Design a scalable backend.

  • Explain Transformers from first principles.

  • Explain how an LLM generates a response.

  • Build RAG without blindly relying on a framework.

  • Diagnose poor RAG retrieval.

  • Evaluate RAG quantitatively.

  • Build an agent with tools.

  • Explain RAG vs fine-tuning.

  • Explain LLM latency/cost optimization.

  • Design an AI system end-to-end.

  • Defend every technology choice in my projects.

  • Complete a technical interview without relying on memorized scripts.

43. Priority Legend

When time is limited:

P0 — Master deeply

DSA
SQL
DBMS
OOP
OS
CN
Backend
FastAPI/Django
PostgreSQL
REST
System Design
ML fundamentals
Transformers
LLMs
Embeddings
RAG
RAG Evaluation
AI Security
Projects

P1 — Strong working knowledge

Redis
Celery
Docker
Kafka
Cloud
Agents
Fine-tuning
LoRA
Quantization
LLM serving
Observability
Distributed systems

P2 — Learn when required

Kubernetes
Ray
Advanced distributed systems
Advanced GPU optimization
Advanced model architecture research
Exotic AI frameworks

44. Daily Revision Checklist

Every study day:

  • 1–3 DSA problems

  • 1 SQL problem

  • 1 CS concept

  • 1 backend concept

  • 1 AI concept

  • Revise yesterday’s mistakes

  • Write down one new interview question

  • Explain one concept without notes

  • Code something without AI assistance

Weekly

  • Re-solve failed DSA problems

  • Review SQL mistakes

  • Review CS notes

  • Review project architecture

  • Build something

  • Do one timed DSA session

  • Do one mock interview

  • Update this checklist

45. Rule for Using AI While Preparing

Use AI as:

Teacher
Debugger
Interviewer
Code reviewer
Architecture reviewer

Not as:

Answer generator

Before asking AI for a solution:

  • Think independently

  • Attempt implementation

  • Identify where stuck

  • Ask for a hint

  • Implement yourself

  • Compare approaches

  • Re-solve later without AI

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