Machine Learning Projects: I contributed to building automated forecasting models that predict future trends based on past data. This involved using machine learning tools and techniques to make our projects smarter and more predictive. Chatbot Development: I led the development of a virtual assistant - chatbot , making it possible for users to interact with an AI avatar. This project was all about making technology more accessible and engaging for users, allowing them to get answers from the avatar in real-time. Improving Code Quality: I worked on updating and fixing older Django code to make it better and more efficient. This task was about making sure our software can grow and improve over time without running into problems because of outdated code. Collaboration: Working together with my team, I helped integrate machine learning solutions into our projects, ensuring that we could use our technical insights to make real-world applications better.

- Web application development with Django/Flask/Laravel. - Improving and cleaning up legacy code. - Database normalisation and performance optimization for SQL queries

I worked on a digital media content product using the following methodology: 1- Brainstorm the best features for getting the highest possible score after communicating with the team. 2- Data collection via web scraping tools (Selenium, ..) 3- Evaluate data through extensive preprocessing using pandas, matplotlib, and seaborn. 4- Examine the best models with AutoSklearn, as well as cutting-edge models and deep learning models such as siamese networks. 5-Analysing the output, post processing(e.g., model calibration, precision-recall tradeoff,...), monitor the system for a while before launching it, handle concept and data drift by validating input criteria.6-Move the code to the production phase using the Flask application, s3 for handling model artifacts and model inputs, and RDS for saving outcomes for later processing.

- Using lambda and ECS services to build microservices - Using Elasticbeanstalk and ECS Services to deploy web applications - Handle heavy traffic with ASG groups/Loadbalancer and configure Nginx/Gunicorn environments - Mailing (SES) and notification (SNS) services - Use CI/CD pipelines to automate steps. - Logging systems with Elastic-search, Cloudwatch

Created and investigated various ML models and cutting-edge models ( Lip Sync, Face Swapping, StyleGAN, and so on.)Using real-time machine learning models, I built a video chat platform with Django channels and extensive features.Different cloud architectures for deployment were developed based on the model's resource requirements (CPU, GPU, Memory usage, Exec time).