Sahilsingh Khalsa

Owned full-stack feature delivery across 3 critical product workflows, building 18+ reusable Next.js/React components to reduce UI inconsistency and cut rework. Designed and implemented FastAPI + Node.js REST APIs with strong validation and error handling, reducing API breakages by 25% and improving request success rates. Optimized MongoDB collections and compound indexing strategies, reducing high-traffic query response times by 30–45% and improving application responsiveness under load. Containerized and versioned 6 microservices using Docker, reducing environment setup time from hours to 30 minutes and enabling reproducible builds across teams. Built and orchestrated GitHub Actions CI/CD pipelines with automated checks and release workflows, enabling weekly deployments while eliminating 40% of manual release effort. Contributed to system reliability through improved logging, validation, and deployment discipline, reducing production risk and improving release confidence.

Increased backend throughput by 40% by developing low-latency Python APIs, enabling AI services to support 10K+ daily users. Improved reliability by 30% by debugging distributed pipelines and applying OOP + DevSecOps practices in production environments. Automated deployments across AWS using Docker, Kubernetes, and GitHub Actions CI/CD, reducing manual toil and release errors significantly. Integrated LLM APIs into production by collaborating with scientists, aligning experimental models with enterprise-grade deployment standards.

Developed a full-stack web application for Bookkeep.com to securely store and share sensitive credentials across distributed teams. The tool replaces insecure sharing methods like email or Slack, enabling users to store and access encrypted credentials without compromising security. Implemented AES-256-based end-to-end encryption, where only the intended recipient can decrypt data using a passphrase that is never stored or transmitted to the server. Even if the database is compromised, the credentials remain unreadable. Built backend APIs using Node.js and Express, managing credential CRUD operations, user authentication (JWT-based), and encrypted data storage in a PostgreSQL database. The APIs were designed to be RESTful and secure, with protection against common web vulnerabilities such as SQL injection and XSS. Used Retool to build the admin dashboard and frontend interface, enabling users to register, log in, view credentials in a sortable/searchable table. Enhanced UX with features like team-based sharing, role-based access control, and email notifications upon credential updates or access events. Deployed the project using Render, allowing for real-time previews, updates, and testing in a live cloud environment. Version control and collaboration are handled through GitHub, ensuring code reliability and iterative development. Documented encryption architecture, setup instructions, and deployment steps to allow other developers or DevOps engineers to self-host or extend the tool for enterprise use.

🔹Designed and implemented scalable full-stack applications using Next.js, Node.js, and PostgreSQL, improving user experience and reducing latency by 30%. 🔹Built modular and reusable frontend components to support rapid UI iterations and A/B testing. 🔹Developed secure, RESTful APIs to power cross-platform functionality and reduce code duplication across services. 🔹Integrated JWT authentication, OAuth 2.0, and role-based access control to enhance security and session handling. 🔹Containerized applications using Docker and orchestrated local dev environments for consistency and team onboarding. 🔹Streamlined CI/CD pipelines using GitHub Actions, achieving zero-downtime deployments in staging and production. 🔹Collaborated with product designers and backend engineers in Agile sprints, contributing to feature specs and sprint planning. 🔹Performed unit, integration, and end-to-end testing using Jest and Postman, increasing code reliability and coverage. 🔹Tuned database queries and indexing strategies, improving API response time for complex queries by over 40%. 🔹Managed cloud infrastructure using AWS EC2, S3, and Route 53, with performance monitoring and logging via CloudWatch.

Led a 40-member team to run 5 technical workshops on DSA and system design, drawing 16 attendees per session and achieving 60% repeat attendance across the series. Mentored 9 peers through DSA prep and resume reviews, helping 3 candidates secure interviews with companies

🔹Developed and evaluated classification and regression models using Scikit-Learn, XGBoost, and LightGBM, improving business metrics and automation pipelines. 🔹Created an end-to-end ML pipeline—data preprocessing, feature engineering, model training, validation, and deployment. 🔹Used grid search and cross-validation to fine-tune model hyperparameters, boosting predictive accuracy from 83% to 92%. 🔹Led the development of an NLP module for automated text classification and insight extraction using spaCy, TF-IDF, and custom rules. 🔹Built and deployed model inference APIs using Flask, achieving <150ms response time under load. 🔹Implemented logging, error handling, and retry mechanisms for production-level model serving. 🔹Developed interactive visualization tools in Streamlit, enabling product managers to interpret and validate model outputs. 🔹Participated in sprint demos and weekly standups with cross-functional teams to present model progress and gather domain feedback. 🔹Applied model interpretability techniques (e.g., SHAP, feature importance) to ensure transparency and business trust. 🔹Wrote clear documentation and handoff guides for future model retraining and scalability.

Courses Completed : Design and Analysis of Algorithms Online Social Network Analysis Big Data Technologies Information Security Natural Language Processing Computer Networks

🎓 Bachelor of Engineering in Computer Engineering | Vidush Somany Institute of Technology and Research 📍Gandhinagar, Gujarat | Graduated May 2024 | GPA: 3.84/4.0 I am a dedicated Computer Engineering student with a strong foundation in cutting-edge technologies and computational methodologies. My academic journey has equipped me with expertise in: - Artificial Intelligence: Designing intelligent systems and exploring advanced algorithms. - Cloud Computing: Implementing scalable solutions and understanding distributed systems. - Computer Vision: Developing applications to interpret and analyze visual data. - Data Science: Harnessing data for actionable insights through machine learning and statistical models. - Operating Systems: Deep insights into system architecture and process management. Passionate about leveraging technology to solve complex problems.