Rajarshi Chakraborty

Brand Voy

Software Engineer

London, United Kingdom

Aug 2026 - Present2 months +

As a Software Engineer at Brand Voy, I contribute to the development and maintenance of scalable, production-ready software solutions and live digital platforms. My role involves working across the software development lifecycle, from feature development and system improvements to debugging, performance optimization, testing, and production support.

• Develop and maintain scalable, reliable, and user-focused web applications for live production environments.

• Work extensively with high-volume sports data, primarily covering cricket and football, to build and support data-driven digital experiences.

• Contribute to sports technology projects serving international clients across English and Vietnamese markets, with a strong focus on localization, usability, and consistent user experiences.

• Work with complex sports data and real-time information to support live sports experiences, including matches, fixtures, scores, statistics, and related content.

• Design and implement new application features while continuously improving existing functionality, code quality, scalability, and system reliability.

• Analyze and resolve technical issues across development and production environments, ensuring stable and reliable application performance.

• Optimize application workflows and data handling processes to support high-volume workloads and responsive user experiences.

• Collaborate with developers, designers, product teams, and other stakeholders to translate business requirements into practical and maintainable technical solutions.

• Follow modern software engineering practices, including clean code, modular architecture, reusable components, testing, debugging, version control, and continuous improvement.

• Contribute to the complete software development lifecycle, including requirement analysis, development, testing, deployment, monitoring, maintenance, and enhancement.

• Gain hands-on experience working on live, client-facing projects where reliability, scalability, performance, and data accuracy are critical.

• Solve real-world engineering challenges while balancing technical requirements, business objectives, application performance, and overall user experience.

This experience has strengthened my expertise in software development, scalable web applications, sports technology, high-volume data processing, real-time sports platforms, multilingual applications, production systems, and modern software engineering practices.

TypeScriptNodeJsReact NativeExpoNextJsExpressPostgreSQLDocker

KubernetesGit

Techno Main Salt Lake

UnderGrad Student Researcher

Kolkata , West Bengal , India

Jan 2026 - Present9 months +

• Forecasting Accuracy: Built and benchmarked LSTM, ARIMA, and RNN models for weather time-series forecasting, achieving a 28.75% improvement in prediction reliability over baseline statistical methods through systematic backtesting on 3+ years of historical data.

• Deployment Pipeline: Refactored research Jupyter notebooks into a modular, deployment-ready Python pipeline (NumPy, Pandas, Scikit-learn, PyTorch, Node.js), reducing the model retrain-to-redeploy cycle from 3+ days to under 4 hours.

• Dataset Engineering: Designed and executed an end-to-end preprocessing workflow across a dataset of 60,000+ rows, covering missing values, outlier detection, seasonal decomposition, and feature normalization, reducing data noise by ~35%.

• Hyperparameter Tuning: Applied grid search and cross-validation across 120+ hyperparameter configurations, boosting LSTM validation accuracy by 19.4% while reducing Mean Absolute Error by 22% on held-out test splits.

• Research Documentation: Authored a structured technical report covering model architecture decisions, evaluation metrics (RMSE, MAE, MAPE), and ablation study results, forming the foundation for an ongoing research publication.

Python

Numpy

Pandas

Scikit Learn

PyTorchNodeJsTime Series Analysis