Ing. Naseeb Grewal

• Assist Scientist with analysing, visualizing data, building ML and Statistical models. • Built AI powered applications by latest Gen AI models to increase productivity and efficiency in workspace. • Built Full stack cost efficient scalabe application for data processing and analysis with AWS, Marklogic, Node JS, React JS, Python, Docker, Gitlab, Jenkin and Terraform.

Creating technical concepts for Generative AI Projects and collaboration with other departments such as UX design, frontend, backend to deliver and focus on specific use case requirements. Configure GenAI Tools and define suitable workflows for scalability, optimization and better user experience.

• Tasked with creating model architecture, building models, and process pipelines for Generative AI use cases. • Built many chatbots powered by organization’s data using MS Power Platforms, Azure Cloud & Langchain • Worked on CI/ CD pipeline along with other developers Important tasks -- Generative AI Architecture Conversational AI Development AI-Powered API designing DevOps for AI Code Quality Review CI/CD Pipeline development AI Collaboration Facilitator

• Query Search Engine using RESTful HTTP API, Docker, and NLP TF-IDF Model • Developed an efficient search engine that handles queries. • Incorporated an NLP TF-IDF model to improve search accuracy and relevant query suggestions. • Successfully implemented an intuitive UI that enables users to input text and obtain multiple related queries. • Developed an RNNs model that can analyze the sentiment of a movie review • Achieved 85% Test Accuracy on a dataset of 25k IMDB Movie Reviews

• Using AWS EC2 and Lambda functions, SageMaker optimized machine learning model deployment through MS PowerApps, leading to 30% faster deployment process and increased team productivity. • Using AWS ECS & Fargate enhanced client data analysis capabilities by 25% through collaboration with data scientists to develop machine learning pipelines, and algorithms, in improving model scalability.

• Improved flight ticket purchase predictions by 15% by constructing a Machine Learning model, leading to more costeffective travel for the users, and assisted in 3 more ML research projects. • Spam Email Detection using Machine Learning & Neural Networks • Published 1st Research paper: Email Spam Detection using Machine Learning & Feature Optimization Method • Evaluated and compared results of Neural Networks, Naive Bayes’, KNN, Decision Tree, Logistic Regression

• Performed ETL on large customer data, Enhanced data inspection and visualization by developing Python codes using Anaconda, resulting in a 30% reduction in data analysis time and improving project efficiency. • Utilized SQL queries and Excel to identify data patterns, leading to a 20% reduction in operational costs and improved efficiency. • Worked on three projects that required the collaboration of different dept. (Business Dept., IT Dept.). • Goals achieved with results better than expected.

• Collaboratively worked on improving IIT Roorkee’s homepage and it’s functionality. • Learned Implementation of Java, C++ and python languages with Data Structures and Algorithms.