data.txt

6-MONTH DOMINANCE ROADMAP Suhaas Karnam | 3-Month Learning Plan | Become The Guy
HONEST FRAMING: 30 hrs per week does NOT make you production-grade at a technology. It breaks the seal —
enough to build with it confidently, explain it in interviews, and ship something real. Mastery comes from using each
tech repeatedly across all 3 projects over 6 months. The week is the ignition. The projects are the engine.
MONTH 1 - Backend Engineering Foundations
Week 1 — SQL at Depth
Goal: Write complex queries confidently without Googling basic syntax
■ Complex joins, subqueries, window functions
■ Indexing, query optimization, explain plans
■ Transactions and ACID properties
Learn from: SQLBolt -> Mode SQL Tutorial -> StrataScratch (practice)
Week 2 — Java + Spring Boot
Goal: Build one working CRUD REST API in Java by end of week
■ OOP in Java - Collections, Exception handling
■ Spring Boot - REST endpoints, dependency injection
■ Ship one small CRUD API before moving on
Learn from: Telusko Java YouTube -> Amigoscode Spring Boot (3hr video)
Week 3 — Docker + CI/CD
Goal: Your existing project runs in a container with an automated deploy pipeline
■ Dockerfile, docker-compose, multi-container setups
■ GitHub Actions - auto test + build + deploy on every push
■ Dockerize one of your existing projects end-to-end
Learn from: TechWorld with Nana Docker YouTube -> GitHub Actions official docs
Week 4 — AWS Core
Goal: Something you built is live on AWS - not just Vercel
■ EC2, S3, IAM, RDS - know what each does and when to use it
■ Deploy a real app on EC2 with RDS as the database
■ Understand VPCs and security groups at a surface level
Learn from: FreeCodeCamp AWS YouTube (full course) -> AWS Free Tier hands-on
MONTH PROJECT — Production-Grade API Platform
Take your Churn project. Add JWT auth, rate limiting, proper error handling. Dockerize it. Set up GitHub Actions CI/CD.
Deploy on AWS EC2 with RDS. Write a clean README. This is the project you walk any interviewer through line by line - it
proves you can take ML work and make it production-ready.
MONTH 2 - Systems Thinking + AI Engineering
Week 5 — System Design Fundamentals
Goal: Explain load balancing, caching, and CAP theorem without hesitation
■ Load balancers, caching with Redis, CDNs
■ CAP theorem - consistency vs availability tradeoffs
■ Rate limiting, API gateway patterns, SQL vs NoSQL decisions
Learn from: System Design Primer (GitHub) -> Neetcode System Design YouTube
Week 6 — Kafka + Redis (Real-Time Systems)
Goal: A local data stream is running - producer pushing, consumer reading
■ Kafka producers, consumers, topics, partitions
■ Redis as a cache and as a pub/sub system
■ Build a small working data stream locally before scaling
Learn from: Confluent Kafka tutorials (developer.confluent.io) -> TechWorld with Nana Kafka
Week 7 — GCP Core
Goal: A containerized app is deployed and running on Google Cloud
■ Key GCP services - Cloud Run, Cloud Storage, Firestore
■ GCP vs AWS - understand when to use which
■ Deploy a Docker container on Cloud Run by end of week
Learn from: Google Cloud Skills Boost (free) - Fundamentals learning path
Week 8 — LangChain + Google ADK
Goal: A single working AI agent that uses a tool and responds to a real query
■ LangChain - chains, tools, memory, prompts
■ Google ADK - agents, routing, tool calling patterns
■ Build one simple agent by end of week - it doesn't need to be fancy
Learn from: LangChain official docs (python.langchain.com) -> Google ADK docs
MONTH PROJECT — Real-Time Data Pipeline + Dashboard
Ingest a real-time stream (financial or IoT), process with Kafka, cache in Redis, persist to PostgreSQL, visualize on Grafana,
trigger anomaly alerts. Deploy on GCP. Pure backend engineering - no ML. This signals systems thinking to any JPMC-type
recruiter.
MONTH 3 - Advanced AI + Visibility
Week 9 — Advanced DSA
Goal: 150 LeetCode done. DP and graph problems no longer feel alien
■ Dynamic Programming - knapsack, LCS, coin change patterns
■ Graph algorithms - Dijkstra, Bellman-Ford, Union Find
■ 4-5 problems per day minimum - quality understanding over quantity
Learn from: Neetcode 150 (neetcode.io) - follow the topic order strictly
Week 10 — ML from Scratch
Goal: A working neural network built in pure NumPy - no PyTorch allowed
■ Implement gradient descent and backpropagation manually in NumPy
■ Build a working feedforward neural network without any ML library
■ Understand what PyTorch is actually abstracting for you
Learn from: Andrej Karpathy - Neural Networks: Zero to Hero (YouTube)
Week 11 — Multi-Agent Architecture
Goal: Supervisor agent routing to 2 sub-agents is working locally
■ Supervisor-worker agent patterns using LangGraph or Google ADK
■ Tool calling, memory, and RAG integration inside agents
■ Agent evaluation - how do you know your agent is actually reliable?
Learn from: LangGraph docs -> Google ADK multi-agent examples -> DeepLearning.AI short courses
Week 12 — PyPI Package + Visibility Push
Goal: Package published on PyPI. One LinkedIn post live. People outside college are using it
■ Package your NumPy ML work as an installable Python library
■ Write tests, docs, and a clean README - treat it like a real product
■ Post on LinkedIn + Reddit r/Python + HackerNews Show HN
Learn from: Python Packaging official guide (packaging.python.org)
MONTH PROJECT — Multi-Agent System with Real Users
Build a hierarchical multi-agent system solving a real problem - research assistant, financial analyzer, or academic tool.
Supervisor routes to specialized RAG agents. Deployed on GCP with a clean frontend and a landing page. Goal: at least 10
real people outside your college actively using it. That is the bar.
Full Checklist at a Glance
Wee
k Technology What You Can Do After
1 SQL at Depth Write complex queries without Googling
2 Java + Spring Boot Build a REST API in Java
3 Docker + CI/CD Containerize and auto-deploy any project
4 AWS Core Deploy on EC2 with a real cloud database
* PROJECT 1 Production-Grade API Platform (AWS + Docker + CI/CD)
5 System Design Explain distributed systems confidently in interviews
6 Kafka + Redis Build and run a real-time data stream
7 GCP Core Deploy containerized apps on Google Cloud
8 LangChain + ADK Build a working single AI agent with tools
* PROJECT 2 Real-Time Data Pipeline + Grafana Dashboard (GCP)
9 Advanced DSA 150 problems done - DP and graphs are comfortable
10 ML from Scratch Neural net in NumPy - understand what PyTorch hides
11 Multi-Agent Systems Supervisor routing to sub-agents working end-to-end
12 PyPI + Visibility Published package + LinkedIn post live
* PROJECT 3 Multi-Agent System with real users on GCP
Every week breaks the seal. Every project builds the mastery. Every month you are harder to ignore.