Great score on Implement LRU Cache Algorithm, Difficulty Medium
Great score on Understanding Rate Limiting in APIs, Difficulty Easy
Great score on Ensuring Real-time Data Accuracy in Mobile App, Difficulty Medium
Developed critical backend features to meet contractual requirements
Implemented go routines and queuing mechanisms to reduce response latency by ~20%
Strengthened logging and monitoring for faster failure detection
Led high-impact initiatives to optimize system performance
Utilized Python for data pipeline reliability
Set up automated tests using pytest/Tavern
Contributed to Invochat and RainScience projects
Built and optimized core APIs for Invochat
Architected and implemented workspace for team efficiency
Integrated ElasticSearch and Redis for optimization
Conducted load testing using JMeter
Developed RainScience project for blockchain workflows
Optimized performance of internal tool Atlas
Added features for standard format coverage
Implemented concave hull algorithm for traffic simulation
Developed key features for Replicar product
Decoupled Protobuf library for faster data population
Created Python script for OSI Protobuf conversions
Deployed Xpressfeed Loader at client sites
Managed data subscriptions and automated email alerts
Worked on DDKoin blockchain project and custom SSH client
Enhanced bitwise operations and introduced gRPC
Built custom SSH client using Qt
Implemented SQLite for in-memory session storage
Developed shell-based UI with QWidgets and X11-forwarding
Developed UI modules and optimized signal plot components
Implemented lexicographic sorting for QLists
Built sensor data viewing solution with synchronized playback
Enhanced graph UI elements for better viewability
Tested, debugged, and optimized critical signal processing modules
Successfully delivered projects across various domains
Utilized a diverse range of tech stacks
Developed an AR-based Carromboard game for Android, enabling users to play against a smart AI bot in an augmented reality environment. Utilized Unity for game development and integrated Google Firebase for backend services.
Implemented a self-driving bot race car using a Convolutional Neural Network (CNN). Trained the bot with a pre-trained model's dataset, enabling autonomous driving. Fine-tuned the model with specific race car data, resulting in highly effective autonomous driving capabilities.
Implemented LSTM and Mixture Density Network techniques to generate realistic handwriting from textual input. Developed an RNN model combining many-to-many and one-to-many approaches, enhancing flexibility and accuracy in capturing handwriting patterns. Achieved impressive results with handwritten samples closely mimicking human writing styles.
Leveraged Decision Trees to develop a robust model for car specification analysis, achieving 98% accuracy on test data. Trained the model using a comprehensive dataset of car specifications. Applied the model to real-world scenarios, providing accurate predictions and valuable insights for decision-making.
Developed a Pacman game in C++ using an open-source GUI framework, implementing accurate gameplay mechanics for an immersive user experience. Utilized a Breadth-First Search (BFS) algorithm to enable intelligent movement and decision-making for Blinky, Pinky, Inky, and Clyde. Ensured the game faithfully adhered to Pacman rules, delivering a classic arcade experience.