• Built the backend of LLM based phone call platform. Developed core chat microservice of the platform on FastAPI, Mongo, Redis stack. Built Langchain-based conversational agents integrating popular LLM services.

• Developed RAG based legal chatbot built on top of the chat service for a legal marketplace for finding lawyers.

• As part of Stony Brook's lab for National COVID-Cohort-Collective project, analyzed large-scale patient data with Spark for informatics purposes. Handled terabytes of data, creating data pipelines, visualizing, drawing conclusions, and presenting the findings to stakeholders.

• Developed model evaluation pipelines for large-scale recommender systems for Snap’s in-house ML platform.

• Implemented counterfactual evaluation methods that can emulate real A/B test results from historical data, thus reducing reliance on A/B tests and saving thousands of hours monthly in iterative development time.

• Adapted ideas from research papers for experiments on internal user data using GCP and BigQuery. Integrated the best methods in Snap’s in-house ML platform using Apache Beam, which outperformed existing metrics.

• Worked under the CTO in a focused team on the development an AI-based cancer diagnosis product. Worked with multiple stakeholders, led 2 junior engineers and collaborated on product roadmap on the way to launch.

• Developed Computer Vision models with Pytorch & Tensorflow for semantic and instance segmentation using weakly supervised techniques. Built efficient data pipelines for model inference on gigascale images on cloud.

• Deployed product on AWS using EC2, SageMaker, Lambda, S3, SQS, DynamoDB etc.