Hi,
I'm
Mayur
Backend Developer
Backend Developer
I'm a Backend Engineer and Computer Engineering student at
JSPM's Rajarshi Shahu College of Engineering, Pune.
I enjoy building scalable backend systems,
distributed job processing engines, and
high-performance APIs using Go, Node.js, Redis,
RabbitMQ, PostgreSQL, and Docker.
My interests include distributed systems, concurrent programming,
backend infrastructure, system design, and performance optimization.
I'm currently seeking Backend Engineering and
Software Engineering Internship opportunities where I
can contribute to production-scale systems.
June 2025 – Nov 2025 | Remote
Technologies I use to build scalable backend systems and distributed infrastructure
A production-grade distributed background job processing engine
inspired by Sidekiq and BullMQ, built for reliable asynchronous job
execution at scale.
✅ Built using Go, Redis, PostgreSQL & Docker
✅ Supports priority queues, delayed jobs & concurrent worker pools
✅ Implemented retry orchestration, DLQ, visibility timeouts & idempotency
✅ Integrated Prometheus, Grafana & structured logging for observability
✅ Load tested with k6 to 10K concurrent submissions (~2.9K req/sec)
Designed for high throughput, fault tolerance, and horizontal scalability
with zero job loss during failure and recovery scenarios.
A backend-first, distributed notification system designed to
reliably deliver messages across multiple channels.
✅ Built using Node.js, RabbitMQ, Docker, MySQL
✅ Supports Email / SMS / Push channels asynchronously
✅ Implemented retry mechanism with Dead Letter Queue (DLQ)
✅ Designed idempotent consumers to prevent duplicate delivery
✅ Added structured logging for failures and retries
Designed for horizontal scalability and validated using
failure and retry simulations.
A real-time backend platform that aggregates and analyzes
software engineering job postings to provide insights into current
market demand and hiring trends.
✅ Built using Node.js, Express, PostgreSQL & Redis
✅ Automated ingestion of 100+ job listings daily across
40+ CS roles
✅ Reduced database query latency from ~500ms to ~8ms (98% faster)
using Redis caching
✅ Implemented background workers for asynchronous data
processing
✅ Added per-IP rate limiting (60 req/min) for reliable,
high-concurrency access
Designed for fast data retrieval, scalable backend processing,
and real-time job market analytics.
A full-stack + ML-based healthcare mapping system that links
NAMASTE medical terms with WHO ICD-11 codes to reduce
manual clinical effort.
✅ Built using Node.js, Express.js, Python (Flask)
✅ ML-powered mapping using scikit-learn
✅ Reduced manual work by 65% through automation
✅ Integrated FHIR generation & analytics APIs
Improved mapping accuracy by 40% with data validation and
preprocessing pipelines.
A production-ready MERN Stack platform built with MVC
architecture for scalable hotel discovery and
management.
✅ Secure authentication using Passport.js with
route protection
✅ Hosts can create & manage listings; users can
browse, view details, and leave reviews
✅ Fully responsive UI with RESTful API integration
and modular codebase for future expansion
Actively tested and publicly deployed — trusted for
delivering a seamless and intuitive user experience.
⚠️ Note: This version focuses on property discovery
and engagement. Booking functionality is not included.
A Node.js script that automates sending personalized job
application emails using data from a CSV file.
It uses Gmail SMTP for email delivery and logs successful
sends into a MySQL database for tracking.
⚡ Achieves 99.99% success rate if email IDs
are valid
🔄 Reduces manual effort by over 95% in
bulk emailing
📤 Capable of sending 400+ emails in one
session (executes slowly to ensure emails don’t go to
spam)
A mini C-based statistical calculator inspired by
NumPy functions.
✅ Computes sums, standard deviations, correlation &
variation for two datasets
✅ Achieved 95%+ accuracy across statistical test
cases
✅ Reduced manual analysis time by 80% vs. traditional
methods
While not a full NumPy replacement, it’s a lightweight C
toolkit and a great foundation for further numerical
computing.
A construction material calculator for estimating
brick, cement, sand, aggregate, and steel needs.
✅ Supports wall, slab, beam calculations with M15–M40
concrete mix
✅ Reduced manual effort by 90% with real-time auto
calculations
✅ Ideal for quick planning in small-scale civil
construction
Helps eliminate over/underestimation in early design stages.
Published on npm • 500+ Downloads
A lightweight Redis-backed job queue library for Node.js focused on reliability and production-ready background processing.
