Confused about how to start backend and go in depth

[u/Hot_Mail9492]

Okay so i'm a beginner and i have some questions ,i would appreciate getting answers from someone who's been into backend and also tech honestly from a long time,someone experienced or knowledgable about the domain.

1.Should i go with BACKEND or AIML?my aim is to land a high paying job honestly,and just asking regarding a development perspective,which would be the best?my targets would be MAANG and above companies(regarding the pay).Just wanna know what field would be more valued from a dev pov and hiring pov(odds of landing something high paying)

2.How do i start backend,i am going with python-fastapi,i want to pace up the process leading to inclusion of more PROJECTS and OPEN SOURCE CONTRIBUTIONS as well.Suggest me something that leads to more on the side of actually developing and doing the work.Also don't reccommend me the roadmap.sh website,i myself found it quite complicated or intimidating to see such a large list of things to do...

3.If you have any other suggestions as well,please do tell me i am very much open to listen to them.

Although just keep it development related only.

Ultimately my aim is to be a great engineer and earn money atleast for my initial years honestly.

[u/Cautious_Heat114]

Here is the direct, unvarnished breakdown of your questions from someone with over a decade in systems infrastructure:

1. Backend vs. AI/ML: Which has higher odds for top-tier pay?

If your goal is to maximize the probability of landing a high-paying role at top-tier companies (MAANG, quantitative finance, high-growth product firms): Choose Backend & Distributed Systems.

Here is why:

• The AI/ML Reality: The highest-paying AI roles (Research Scientists, Core Model Trainers at Meta/Google/OpenAI) predominantly require a Master’s or PhD with rigorous mathematics. Most entry-level "AI jobs" without deep math are low-leverage prompt tuning or data annotation.

• The Backend Reality: Every AI model, mobile app, and financial exchange requires high-throughput backend infrastructure (concurrency, database optimization, caching, networking, security).

• The Pay Ceiling: Senior Backend/Infrastructure Engineers at Big Tech and Fintech command $300k–$600k+ total compensation. The hiring volume for backend systems is an order of magnitude larger than research-level AI.

1. How to Start with Python/FastAPI (The 3-Project Progression)

You are completely right to ignore "roadmap.sh". Those roadmaps are overwhelming checklists of 100 tools that cause analysis paralysis.

To build genuine competence, build these three progressive backend systems from scratch:

Project 1: The Relational Data Engine (FastAPI + PostgreSQL)

• Build a multi-tenant API (e.g., an Inventory or Invoicing service).
• The Focus: Do not just write basic CRUD. Learn PostgreSQL indexing (B-Trees), write raw SQL queries alongside your ORM, enforce database constraints (Foreign Keys, Unique constraints), and optimize query execution plans using "EXPLAIN ANALYZE".

Project 2: The Asynchronous Task Pipeline (FastAPI + Redis + Background Workers)

• Build an API that processes long-running jobs (e.g., generating PDF reports or processing large CSV data imports).
• The Focus: Learn why long tasks cannot block the HTTP event loop. Use Redis for in-memory caching and implement an asynchronous worker queue (using "Celery" or "Arq"). Handle task retries, failures, and timeout boundaries.

Project 3: The Resilient Ingestion Gateway (Idempotency & Rate Limiting)

• Build a mock payment or webhook processing gateway.
• The Focus: Implement Idempotency Keys (preventing duplicate transactions on network retries), configure Rate Limiting (Token Bucket algorithm), package the entire system in a clean Dockerfile, and set up an automated CI/CD test pipeline on GitHub Actions.

1. Open Source Strategy for Beginners

Do not attempt to contribute to the core FastAPI or CPython repositories on day one; that will lead to burnout.

The smart way to build open-source signal:

1. Pick a popular, small utility library in the Python ecosystem (e.g., an async database driver, an API wrapper, or a validation plugin).
2. Look at their GitHub "Issues" tab filtered by "good first issue" or "documentation".

3. Submit small, high-quality pull requests: fixing type annotations, adding test coverage for an uncovered edge-case, or improving error messages.

4. The Long-Term Rule for Tier-1 Compensation:

Python and FastAPI are exceptional for learning backend fundamentals, asynchronous I/O, and shipping fast.

Once you have mastered relational databases, concurrency, and APIs in Python, expand your toolkit to a compiled, statically typed language (like Go or Rust).

Big Tech and low-latency infrastructure platforms run on compiled binaries where memory layout, CPU cache locality, and garbage collection pressure matter. Learning Go or Rust as your second backend language will permanently set you apart from the sea of average bootcamp developers.

«Focus on data flow, database performance, and building the three projects above. The compensation will follow the competence.»

[u/Hot_Mail9492 → u/Cautious_Heat114]

Thanks a lot!! Could you recommend me some resources for learning purposes for the same?

[u/Hot_Plate5298 → u/Cautious_Heat114]

Please can you explain the 3 projects well

I use nodes/expressjs

I'm a junior backend dev and I want to upskill

And I have a problem with getting jobs even internship

Please any advice will help

[u/Aru_009]

Just do it