Hi, I'm Nilesh.
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CS undergrad at SRMIST Chennai — building backend systems, ML pipelines, and cloud infrastructure. Currently running three internships in parallel across healthcare AI, data engineering, and industrial AI.
I'm a Computer Science student at SRM Institute of Science and Technology, Chennai, with a deep focus on backend engineering and distributed systems.
I enjoy working at the intersection of software infrastructure and applied ML — building things that are reliable, observable, and fast. Whether it's a RAG-based AI agent, a multi-threaded Java microservice, or an ETL pipeline at scale, I care deeply about production quality.
Outside of code, I contribute to GDG Chennai, lead web for ACM SIGKDD SRMIST, and am recognized as an AWS Community Builder for cloud advocacy.
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Oct 2025
- Contributed to healthcare technology projects under the Google Summer of Code ecosystem using Java, Maven, Git, and the OpenMRS SDK, collaborating with a global open-source community on feature development, debugging, and issue resolution.
- Worked on backend and healthcare interoperability components involving MySQL, Hibernate, Docker, and FHIR integrations while actively participating in remote agile workflows, community discussions, and open-source contribution practices.
Aug 2025
- Containerized entire codebase with Docker; deployed on Azure with auto-scaling and high availability.
- Built Heally AI — RAG chatbot with vector knowledge base for context-aware healthcare query responses, reducing manual support load.
- Owned end-to-end AI + DevOps pipeline delivery from development to production.
Dec 2025
- Built Power BI dashboards tracking 20+ KPIs → 25% improvement in onboarding completion rates.
- Automated ETL pipelines for 100K+ records/day, cutting manual reporting time by ~40%.
- Findings translated into product fixes reducing support issues by 30%.
Jul 2025
- Built Digital Shift Handover System replacing error-prone manual processes in marketing division.
- Random Forest + anomaly detection at 85% accuracy vs. 60% manual baseline.
- Time-series analysis on shift data to flag recurring bottlenecks proactively.
Production-deployed RESTful microservice on AWS EC2. Concurrent notification dispatcher (ExecutorService + BlockingQueue) processing 500+ notifications/min. MySQL composite indexing cut API response from ~200ms to ~120ms.
FastAPI backend with AI agents for real-time climate risk scoring across 50+ cities at sub-second response. Telegram bot enabling push alerts without a web frontend. One-command Docker deployment.
LSTM + XGBoost ensemble on 10K+ SKUs — 18% forecast accuracy gain. Real-time dashboards reduced projected logistics costs by 12% through scenario-based analysis.