● Extensive experience in Software development life cycle, including coding standards, code reviews, source control management, build processes, testing and operations experience.● Software engineering experience building consumer products.● Working with product and design to evaluate, solution, and implement new features, driving the full development lifecycle from idea to release across the frontend and backend.- Using GitHub Actions and AWS for code deployment● Dockerizing and scaling applications.● Collaborating with a team to develop LLM As A Service to provide a GPT like experience for educators. The teamuses Agile methodologies, focusing on rapid iteration and continuous improvement.● Designed and developed the Retrieval-Augmented Generation (RAG) architecture that is central for the product.● Built and deployed BERT embedding models through Keras Deep Learning Framework on AWS Lambda andcreated a Golang orchestration service for querying Pinecone Vector Database and LLM● Created a context aware interactive chatbot in TypeScript for customers and stored conversations, queries andinformation in AWS DynamoDB.● Created Python data pipelines through Dagster for preprocessing tasks (NLTK’s Tokenization, Lemmatization,Stemming, etc.) and embedded text with GloVe into matrices to ease training.● Expertise in Kubernetes, Docker, CI/CD, AWS, and relevant scripting languages● Strong grasp of Infrastructure as Code (IaC) principles and tools.

- Worked with the team on building a category-defining product and scale it.- Passion for approaching large-scale problems through small, data-informed, and experiment-driven iterations- Experience with Java and coding using object oriented concepts.- Experience writing APIs and interacting with relational databases (Postgres)- Knowledge in Containerization and Cloud Services (Docker, Kubernetes).- Using AWs for cloud services- Good knowledge of web technologies (such as HTTP, HTML/DOM, JavaScript, TypeScript)

- Created an application that matches RPI students to tutors through a Recommendation Machine Learning Algorithm- Tech Stack included MongoDB, ExpressJS, ReactJS and Node.js (MERN). Version Control was donethrough Git and was further tracked using webhooks to ping Slack..- Implemented stateless authentication using JWT and Oauth2 to enable horizontal scaling in the future.- Created a Type-ahead Search Bar by using a Trie to rank and suggest 5 of the most popular searches in150ms.- Designed and implemented a schedule comparing tool by inserting busy intervals in a Red-Black Treeand displaying free times in under 200 ms.

- Created a private API in Python that provides third party users interpolations, statistical modeling, andgeneral analysis tools to implement Smart Manufacturing.- Orchestrated microservice architecture sagas by exposing a GraphQL API Gateway to launchasynchronous interprocess communication calls.- Leveraged Github action pipelines for seamless Continuous Integration/Deployment (CI/CD) to Azure.