Software Engineer
Summary
Leading a team of four in the APAC region, ensuring seamless uptime and performance for critical, market-impacting applications, maintaining 99.99% availability. Developed an event-driven serverless cloud native service from end to end, scaling to 9M+ daily events with AWS CDK (Lambda, DynamoDB, SQS, S3), built with Python and TypeScript, achieving $600K+ annual savings and 40% lower processing latency. Developing a Generative AI powered internal search assistant using RAG on LLM models, enabling instant retrieval of documentation, reducing search time from 3+ hours to seconds, and boosting developer productivity by 5x. Developed a real-time risk assessment platform in Golang, integrated into the firm's centralized risk management system, providing instant risk analysis for 40K+ employees. Built a real-time monitoring dashboard using Grafana, Prometheus & Thanos, improving SLA/SLO tracking, and increasing client transparency. Architected and Developed an automated workflow based break-glass service for users with Java 21 & Spring Boot, cutting manual intervention time by 80%. Migrated a service to Kubernetes Managed Deployment, implemented ArgoCD for automated CI/CD, reduced build time to seconds, and streamlined log retrieval and debugging. Core Tech: Java 21, Spring Boot, Python, TypeScript, React.js, Golang, AWS, PostgreSQL, Gen AI.
Software Dev Engineer Intern
Summary
Contributed to the development of the Alexa Mental Health Checker Skill called the Mind Plan. Successfully Implemented the feature for categorising activities within the Alexa skill, which acted as a 3X faster way for users to get access to the NHS Content. Executed feature gating, effectively preparing it for the global launch using AWS Evidently. Utilized tech stack: Java, TypeScript, APL, AWS Lambda, CloudWatch, Evidently, Alexa SDK, etc.
SDE Intern
Summary
Implemented a resilient internal notifications service, achieving a 30% reduction, emphasizing robust architecture and SMTP-based email delivery with Spring Boot. Ensured the reliability of the service through testing, including writing unit tests and utilizing Mockito.
Bachelor of Technology(B.Tech)
Computer Science and Engineering(CSE)
Awarded By
Palantir
C++, Java, Python, Golang, TypeScript, HTML/CSS, JavaScript, SQL.
Next.js, Lang Chain, Spring Boot, Django, Node js, Terraform, FastAPI.
AWS, Apache Kafka, Kubernetes, PostgreSQL, Git, Linux, AWS, RAG, DevOps, ArgoCD.
Summary
Graspo AI is an educational platform built on Retrieval-Augmented Generation (RAG) technology, featuring custom-trained models for PDF processing and Tesseract integration. The system uses Qdrant vector database for semantic search and has intelligent PDF chunking for document handling. With domain-specific generative AI models and a Next.js frontend, it delivers high-performance, context-aware learning capabilities.