Ritik Gupta

About Ritik Gupta

Professional Skills and Experience:3 years of professional experience building products end to end-Software Engineer with good knowledge of backend -Developed Event-driven Microservices in Python using message queues (rabbitMQ) and Pub-Subs (Kafka)-Developed data pipelines in Python using different Synchronous and Asynchronous frameworks-Experience in building extensible and scalable applications for a high volume of traffic-Good problem-solving skills with an interest in algorithms, data structures and system architecture-A Tech-Stack agnostic person who can quickly grasp new language/technology/tools in a short period.I have undertaken various projects, ranging from developing software applications for small businesses to building complex systems for enterprise clients. I take pride in my ability to analyze complex problems and provide innovative solutions, leveraging cutting-edge technologies to deliver robust and scalable software products.Proficient in mapping business requirements, designing customized solutions, and planning test strategies with strong analytical skills and the ability to analyze business practices and define optimal procedures or practices.I thrive in fast-paced environments, where I can work collaboratively with cross-functional teams to deliver exceptional results. As a software engineer, I am passionate about staying up-to-date with the latest trends and advancements in the industry, which allows me to bring fresh ideas and perspectives to each project.

Experience

1. Associate Data Scientist

   NoBroker.com

   Jan 2024 — Present · Bengaluru, IN

   Led end-to-end R&D and deployment of a recommendation engine using Word2Vec embeddings (+15% conversions) and XGBoost re-ranking (+35% uplift)• Built and optimized a custom EasyOCR pipeline (92%+ accuracy, WER <8%, CER 80% precision and recall across all classes• Designed a graph-based prompt optimization framework (Leader–Follower LLM paradigm) improving consistency by 25%, accuracy by 18%, and evaluation speed by ~40%• Built an RL-based optimization system using Genetic Algorithms and ε-greedy exploration, improving throughput by ~25% and reducing failure rates by ~30%• Re-architected scraping infrastructure to a lightweight HTTP-based distributed pipeline (500GB/day, 5M+ events/day), boosting throughput by 90%, reducing latency by 60%, and cutting costs by 35%• Developed proxy orchestration for 10K+ concurrent daily requests, reducing third-party dependency costs by 70%• Performed dynamic reverse engineering of Android apps using Frida to trace runtime data flows and mitigate sensitive data leakage

Education

• Rajiv Gandhi Prodyogiki Vishwavidyalaya

  Bachelor of Engineering - BE, Electronic and Communications Engineering

  2016 — 2020

• Tutort Academy

  Advance AI & ML Masters Program