Developed RESTful APIs using Django to enable seamless data flow between trading platforms and external data sources.Implemented Swagger for comprehensive API documentation, ensuring clear and accessible integration guidelines for users and developers.Designed and implemented ETL pipelines on AWS using Lambda to automate the extraction, implemented error-handling mechanisms transformation, and loading (ETL) financial data from various sources. Utilized Amazon S3 for secure and scalable data storage, ensuring efficient and timely data processing along with seamless transfer of large datasets.Integrated Amazon RDS to manage and store transformed data, optimizing database performance and enabling quick access for analytical queries, which streamlined the overall data workflow and enhanced reporting and decision-making.Collected and preprocessed large sets of financial data using Pandas and NumPy, including historical market data, price feeds, and tick data, to prepare for analysis and enhance data consistency for model input.Analyzed market trends, patterns, and volatility using statistical and machine learning methods, improving the predictive accuracy of trading algorithms.Leveraged AWS SageMaker to develop, train, and deploy machine learning models, utilizing built-in algorithms and custom frameworks for analyzing large datasets to enhance trading strategy predictions.Optimized performance and reduced latency by enhancing code and infrastructure, vital for high-frequency trading and fast data processing.Utilized AWS services for deployment and Git for version control, establishing efficient CI/CD pipelines for smooth application updates and scalability.Worked closely with cross-functional teams to ensure alignment in algorithm development and strategy execution

• Developed and maintained complex financial models supporting forecasting, budgeting, and report generation using Python, ensuring accuracy and scalability of financial projections.• Streamlined and automated data processing workflows in collaboration with the IT department by developing Python scripts to optimize data entry, while also integrating tools and applications with GitHub to enhance workflow efficiency and team collaboration across various projects.• Developed and maintained RESTful APIs using Python (Flask) to facilitate seamless communication between various services and application components, ensuring real-time data integration.• Developed and implemented ETL processes using AWS Glue to extract, transform, and load data stored in S3 buckets into AWS RDS for efficient analytics.• Collaborated with testers to develop and implement test cases, scenarios, and features using Python’s unittest and PyTest for a TDD (Test Driven Development).• Implemented AWS CodeDeploy integrated with GitHub to ensure efficient application deployments on AWS, optimizing the efficiency of rollouts and updates.• Designed, developed, and published data sources and dashboards using Tableau to visualize key financial metrics and performance indicators.

• Applied machine learning algorithms (Logistic Regression, SVM, Decision Trees, and Random Forest) for document categorization, enhancing classification accuracy.• Employed NLP techniques using NLTK and spaCy for text preprocessing, while utilizing scikit-learn for model implementation and performance improvement.• Developed robust ETL pipelines in Python to automate data extraction, transformation, and loading into MySQL databases for further analysis.• Utilized Python's Pandas for efficient data manipulation and cleaning, ensuring high-quality input data for machine learning models, and NumPy for numerical operations on large datasets.• Created a web application with Flask, using REST APIs to provide users with an intuitive interface for interacting with the document classification models, streamlining document submission, classification retrieval, and real-time feedback.• Built interactive data visualization applications using Streamlit, enabling stakeholders to visualize model outputs and performance insights.• Employed Matplotlib and Seaborn for static visualizations and exploratory data analysis (EDA) to understand data and model performance metrics.• Collaborated with team members on GitHub for version control, ensuring efficient management of code and documentation throughout the project.