I am currently pursuing a Bachelor of Science in Data Science and Applications at the Indian Institute of Technology Madras, expected to graduate in 2026. Throughout my academic journey, I have developed a strong foundation in data engineering, machine learning operations, and deep learning, complemented by practical experience in econometrics, quantitative finance, and market research. I am passionate about leveraging AI and cloud technologies to build scalable and efficient systems.
As an AI Engineer Intern at Vsynergize, I have designed and optimized stateful multi-agent workflows on AWS Bedrock and architected production-grade retrieval-augmented generation microservices using FastAPI and Qdrant. My work includes engineering scalable backend pipelines with AWS SQS, Lambda, and DynamoDB to handle asynchronous bulk document ingestion with transactional safety.
I have also improved system performance by refactoring core telemetry and endpoints to use Server-Sent Events streaming, reducing latency by 91%. My projects span retail analytics, fintech banking applications, AI-powered job hunter platforms, and fine-tuning large language models across multiple Indian languages.
I am skilled in Python, SQL, PostgreSQL, and cloud platforms such as AWS and Google Cloud. I have hands-on experience with containerization tools like Docker, and I am proficient in using FastAPI, Redis, and Qdrant for backend development. I have also worked with ML frameworks such as PyTorch and Hugging Face for model training and optimization.
In addition to my technical skills, I have earned professional certifications in AWS Data Engineering and Cloud Foundations, as well as advanced AI certifications from NPTEL (IIT). I have competed in national-level techathons and independently managed a cryptocurrency portfolio, applying structured market analysis and risk assessment frameworks.
I am committed to continuous learning and community leadership, having contributed as a core team member for Kanha House and successfully managed logistics for regional student meetups. I am eager to apply my skills and knowledge to solve complex AI and data challenges in a dynamic work environment.
Designed and optimized stateful multi-agent workflows on AWS Bedrock and architected a production-grade RAG microservice using FastAPI and Qdrant. Engineered scalable backend pipelines utilizing AWS SQS, Lambda, and DynamoDB to handle asynchronous bulk document ingestion with transactional safety. Refactored core telemetry and endpoints to use Server-Sent Events streaming, reducing latency by 91%.
Bachelor of Science in Data Science and Applications
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