> Developed a personalized Goal template recommendation system using Collaborative Filtering and Matrix Factorization to enhance user engagement, resulting in a 25% increase in click-through rate (CTR).> Fine-tuned OpenAI API parameters to create dynamic recommendations of mentors and goal templates to mentees with high relevance by 90%.> Led the design and deployment of a content-based recommender system for an online mentor-mentee platform, enhancing mentors and mentees experience by generating personalized goals, books, courses and events suggestions, which improved goal completion rates by 20%.> Collaborated with data scientists and engineers to optimize Transformer-based models (BERT, GPT-3 and GPT-4) for text generation tasks, reducing model inference time by 30% while maintaining high-quality outputs.> Built and deployed a Neural Collaborative Filtering (NCF) model for recommending products based on user-item interaction data, achieving a 50% lift in purchase conversion rates.> Integrated a Retrieval-Augmented Generation (RAG) system, using OpenAI API, Llama, Hugging Face and LangChain for document retrieval and GPT for response generation, improving the relevance of long-form recommendations by 20%.> Experimented with Variational Autoencoders (VAEs) for generating new content-basedrecommendations, leading to 12% higher recommendation novelty.> Incorporated a Reinforcement Learning algorithm to dynamically adjust recommendations in real-time, improving user interaction time by 18%.> Integrated a natural language processing (NLP) pipeline to analyze customer reviews and fine-tune recommendations based on sentiment.

> Developed an automated AWS pipeline solution that processed multiple terabytes of data per month.> Designed and maintained Datalakes to ingest terabytes of data. Designed to serve as source of truth for the enterprise> Implemented end-to-end ELT / ETL architecture and pipelines for analyzing large data sets, identifying patterns and relationships in AWS.> Built the foundational data infrastructure for analytics, reporting, and machine learning data pipelines and products> Generated high-volume mock data using Faker and Scrapy> Data cleanup using automated optimized filtering > Implemented Benchmarking, Performance optimization and Monitoring using Locust and monitoring tools.> Database creation and maintenance for RDBMS (PostgreSQL) and Document database (MongoDB)

> Guiding Engineers in testing UI and backend databases.> Identified scalability constraints and helped the team to establish the process for application scalability, reliability, security. This enhanced the user experience and growth. > Worked with engineers to implement the validation process of data and UI.> Provided guidelines in implementing SRE for testing and production systems> Worked with the development team to adopt DevSecOps (including IaC & CI/CD) for deployment.> Worked with the executives and the engineering team to move the app into cloud

> Worked with users and engineering team in India to identify the pain points and improved the Kavigai user interface.> Helped creating training and onboarding artifacts for easy enrollment and adoption> Implemented HCD process. Conducted user interviews to improve the user experience. > Worked with CEO and engineers to enable fast paced sprints.

> Overseeing the development of R&D products to achieve the best performance goals for E-Vehicle automotive> manufacturers and developing strategy and roadmap focusing on E-Vehicle manufacturer's revenue growth.> Accelerate the production of innovative Automated Test Systems (ATS) for the Electronic Control Unit (ECU) by 60% and in the course of development,> Assembled a team to implement a customer project to collect metrics on store walk-in traffic and sales analytics. This device is deployed in more than 100 stores nationwide.