● Led the development of an e-learning platform that enhances AWS learning with rich video experiences.● Built a client-facing application using Next.js with high streaming capabilities.● Created a video management portal for users to upload videos and monitored the video processing pipeline.● Directed a team of three in building a serverless video processing pipeline using AWS CDK and TypeScript.● Worked with Applied Scientists to convert video-processing Python scripts into AWS Lambda functions.● Developed unit and integration tests for the video processing pipeline using Jest and Pytest.● Created a custom video player component with React, integrating AI capabilities like transcripts, deep dives and image OCR.● Improved accessibility and internationalization by following WCAG guidelines and providing translatable text content.● Integrated TensorFlow and NLP models for complete transcript and OCR solutions across full-stack applications.

● Led the development of an AI-driven project for the drive-thru systems in the restaurant industry. ● Spearheaded the design and implementation of RESTful APIs using Django REST Framework, enhancing system integration and data exchange capabilities.● Leveraged Django's ORM for efficient database interactions, ensuring robust and scalable back-end architecture to handle audio input, process through the NLP pipeline, and return structured order data.● Built a web-based interface using Django template views to render HTML and CSS, as well as a mobile app communicated with the server using a REST API built with the Django REST Framework.● Utilized JWT tokens built by the Django Rest Framework SimpleJWT library to authenticate users when using the REST API.● Built responsive and intuitive user interfaces on the restaurant frontend using React 18+ with TypeScript.● Integrated NLP and Speech-to-Text models using Google Cloud and Python to capture and process verbal orders with high accuracy.● Improved data pipelines using Pandas and Apache Airflow, enhancing data processing and scheduling capabilities for the application.● Developed a real-time data processing system using WebSocket and predictive analytics with TensorFlow, dynamically updating menu boards based on inventory, promotions, and time of day.● Utilized PostgreSQL to store historical order data for analysis and machine learning algorithms such as Random Forest for predicting peak order times and staffing needs based on historical data.● Played a pivotal role in database schema design, optimizing for performance and scalability and implementing complex SQL queries for data manipulation and retrieval.● Enhanced a comprehensive data processing and analytics module using Python, Pandas, and NumPy for efficient data manipulation and analysis.● Integrated Azure services to enhance cloud capabilities, streamline deployment, and optimize resource management for scalable web applications.

● Spearheaded the development of telehealth web and mobile applications with AI capabilities from scratch.● Developed conversational AI chatbot for customer support, implementing advanced NLP techniques to enhance user interactions.● Built a real-time engine using Pytorch and deployed it on AWS Lambda to personalize the user experience for an AI-powered recommendation engine.● Developed medical report analyzer by collecting and refining data from medical reports to recalculate disease timelines and analyze real-time effects by using Python and WebSocket.● Implemented responsive and pixel-perfect UIs using React 18+ with TypeScript and React Native (Expo).● Utilized Node.js, and Flask framework for building micro-service backend infrastructure, establishing the communication between the micro-services using RabbitMQ.● Experienced using NLTK for text preprocessing, tokenization, stemming, and lemmatization.● Proficient in TensorFlow for building and deploying neural networks, including CNNs and RNNs.● Integrated TensorFlow models into full-stack applications using Flask for seamless API interactions.● Successfully implemented multi-lingual real-time text and video chat application with AI integration using WebSocket, and WebRTC.● Participated in building a multi-lingual real-time communication and streaming platform creating a highly scalable and responsive web application using Next.js, Jotai, Node.js, GraphQL, and PostgreSQL with TypeScript.● Utilized technologies such as Docker and Kubernetes to containerize microservices, enabling seamless deployment and management of services in a cloud-native environment.● Implemented data caching strategies using Redis, reducing page load times by 20% and enhancing overall website performance.● Utilized an IaC (Infrastructure as Code) tool-Terraform for application infrastructure.● Managed AWS services like EC2, ECS, Lambda, IAM, SQS, RDS, DynamoDB, Cognito, Amplify, Media Live, and Media Converter.

● Architected and implemented a scalable app for a finance platform-car dealership platform using Python/Flask.● Developed data analytics tools using Python Analytics APIs and machine learning from existing financial data to provide dealerships with insights into market trends and consumer behavior, enhancing decision-making.● Planned process to convert projects from Python 2 to 3 and update multiple projects.● Worked with the QA team to improve applications by introducing enhancements needed by dealers including vehicles, parts, Lexus vendor interface tracking tool, and customer records.● Built integrations to credit reporting, money transfer, and client CRM services for projects.● Created a real-time fraud detection system using machine learning techniques to identify and prevent fraudulent transactions.● Migrated a Flask application to the Django framework, fortifying security measures and boosting system functionality.● Developed RESTful APIs for transaction handling, implemented transaction queues using Redis, and optimized PostgreSQL queries for performance.● Created and documented APIs for external integrations, managed OAuth2 flows for secure access, and developed webhooks for real-time data synchronization.● Developed a real-time chat system for customer support, set up serverless functions for automated responses, and integrated with third-party CRM tools.● Set up CI pipelines in Bitbucket to automate code health checks, tests, and deployments.

● Assisted in developing real-time notification features for alert users about price drops, new listings, or messages from dealerships.● Collaborated with senior engineers on debugging and troubleshooting issues, improving technical skills and problem-solving abilities.● Participated in code reviews and pair programming sessions, gaining exposure to agile development methodologies.● Supported the QA team in testing new releases, identifying bugs, and verifying fixes to maintain high product quality.● Utilized the basics of the Python language including data structures, algorithms, and loops, as well as the basics of web development and APIs.● Learned how to use basic data structures in Python such as lists, tuples, and dictionaries effectively and when to use each one depending on the use case in code.● Learned basic algorithms, functions, loops, recursions, and how to encode and decode JSON data using Python.