Hello World, I'm

Aalind Singh

I build |

A Backend Engineer with 2.5+ years of experience building high-performance distributed systems using Python, Golang, and AWS. Specializing in asynchronous data pipelines, database query optimization, and RAG-based AI solutions.

Explore Work Download Resume

bash

~ node --version

v24.19.0

~ cat developer.json

{ "name": "Aalind Singh", "focus": ["Distributed Systems", "Edge Computing"], "likes": ["Fast Code", "Aesthetics", "Vanilla CSS"] }

About Me

A brief overview of my engineering philosophy and technical competencies.

My Background

I am a backend engineer focused on building robust, high-performance distributed systems. Rather than focusing on a single silo, I enjoy working across multiple domains—combining my core backend expertise in Python and Go with large-scale data pipelines, machine learning workflows (like vector search and embeddings), and asynchronous queue architectures.

When tackling technical obstacles, I lean towards deep-dive investigation (analyzing database explain plans, memory profiles, and server-side cursor states) rather than quick hotfixes. I document my real-world discoveries and troubleshooting techniques in my personal blog.

I'm always eager to learn new technologies and apply system-level optimization patterns to scale services efficiently and solve complex business logic challenges.

Core Technical Stack

Technical Skills

PythonGolangNodeJSOOPSDBMSSDLC

Libraries & Frameworks

FastApiGinDjangoDRF (Django Rest Framework)SeleniumCeleryLangchain

Databases & Cloud

PostgreSQLMySQLAWSMongoDBRedis

Tools & OS

VSCodeGit/GithubPyCharmPostmanCursorDockerLinuxWindows

Featured Projects

A curated selection of core tools and services I've developed.

AI Knowledge Assistant (RAG)

A Retrieval-Augmented Generation (RAG) platform implementing document ingestion, chunking, embeddings, and vector search to enable context-aware question answering. Designed a scalable retrieval pipeline supporting asynchronous document indexing, metadata filtering, and low-latency semantic search.

FastAPIPythonPostgreSQLRedisCeleryQdrantDocker

Node.js/Puppeteer Web Scraping Platform

A production-scale web scraping engine processing over 200,000 websites monthly. Built as a fault-tolerant service with semantic data extraction, customized pagination, and robust error recovery routines.

Node.jsPuppeteerJavaScriptDockerWeb Scraping

Distributed Task Pipeline

A Celery and RabbitMQ asynchronous job processor optimized for long-running workflows. Features idempotent step execution, retry safety, and multithreading, reducing task times by 40%.

PythonCeleryRabbitMQDjangoPostgreSQLMultithreading

View GitHub Repository