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πŸš€ About Me

Name: Krish Kumar
Role: Backend & Cloud-Native Engineer
Institution: Thapar Institute of Engineering and Technology (CSE Undergrad)

Core_competencies:
  - Distributed Microservices & Async I/O
  - Kubernetes Operators, Helm & GitOps
  - High-Concurrency WebSockets & Pub/Sub
  - Database Normalization (BCNF) & Atomic Transactions

Currently_building: CapstoneConnect – real-time student collaboration platform
Currently_learning: Service Meshes (Istio) & eBPF observability
Fun_fact: "Resolved race conditions across 100+ simulated concurrent requests – and lived to tell the tale"

🌈 Tech Stack

Languages

Python C++ Rust JavaScript SQL

Backend

FastAPI Flask WebSockets SQLAlchemy

Cloud & DevOps

Kubernetes Docker Helm ArgoCD GitHub Actions AWS

Data & Observability

PostgreSQL Redis Prometheus Grafana


πŸ”₯ Featured Project

πŸ”— CapstoneConnect

Real-Time Student Team Discovery Platform

Architected a 6-service microservices backend on Kubernetes with real-time bi-directional chat via Redis Pub/Sub and WebSockets – engineered for stability under 500+ concurrent connections.

FastAPI Β· Kubernetes Β· Redis Β· PostgreSQL Β· WebSockets


πŸš€ Other Projects

☁️ Nimbus – K8s Self-Service Deployment Engine

Self-service PaaS letting developers ship apps without raw K8s YAML. Custom Kopf Operator in Python managing 3 CRDs, with Kaniko for rootless in-cluster image builds.

Python Β· Kubernetes Β· Docker Β· Kaniko

πŸ”’ Secure Share – Encrypted File Sharing

Async, low-latency binary file distribution with a Rust backend and TypeScript client. Chunked transfer encoding and strict payload verification.

Rust Β· TypeScript Β· Security

πŸ₯ Hospital Management System – Healthcare Portal

BCNF-normalized 8-table MySQL schema with 7 stored procedures, 3 functions, 6 triggers guaranteeing atomic appointment scheduling. Flask + SQLAlchemy service layer.

Flask Β· MySQL Β· SQLAlchemy

✈️ Rolls-Royce Turbofan RUL Prediction – Predictive Maintenance

Predicts Remaining Useful Life of turbofan engines on NASA's C-MAPSS dataset using a Bidirectional LSTM with self-attention and Monte Carlo Dropout for uncertainty-aware predictions, deployed as an interactive Gradio dashboard.

Python Β· TensorFlow Β· LSTM Β· Feature Engineering & Selection Β· Model Evaluation Β· Gradio

πŸ“¦ Supply Chain Risk Intelligence – ML Classification

Multi-class model predicting e-commerce delivery outcomes (late, on-time, advance, canceled) using SMOTE-balanced data and comparing XGBoost, Random Forest, KNN, and Logistic Regression with SHAP explainability, deployed via Gradio.

Python Β· XGBoost Β· Pandas Β· NumPy Β· Scikit-Learn Β· Matplotlib


More projects on GitHub β†’


πŸ’‘ Architecture Principles

  • πŸ›‘οΈ Defensive & Idempotent Design – every state-changing endpoint tolerates partial failure and concurrency
  • ☸️ Declarative Infrastructure & GitOps – manifests, Helm charts, and ArgoCD pipelines for repeatable deploys
  • ⚑ Database Efficiency First – normalized schemas, stored procedures for atomic ops, Redis caching
  • πŸ“Š Telemetry & Observability – Prometheus + Grafana before incidents reach users

πŸ“Š GitHub Stats


"Complexity is the enemy of reliability. Build simple, robust, and scalable systems."



Pinned Loading

  1. Secure-share-frontend Secure-share-frontend Public

    TypeScript 1

  2. Secure-share-backend Secure-share-backend Public

    Rust 1

  3. axum_auth_backend axum_auth_backend Public

    Rust 1

  4. Time-Capsule-Backend Time-Capsule-Backend Public

    Rust 1

  5. axum-todo-API axum-todo-API Public

    Rust 1

  6. Software-engineering-project Software-engineering-project Public

    1