Madhu P

• Designed and developed ML-powered backend services using Python (FastAPI/Flask) for financial data processing and analytics. • Built ML pipelines using Scikit-learn, TensorFlow, and PyTorch, improving predictive analytics for financial datasets. • Implemented data processing and transformation pipelines (ETL-style workflows) using Python, Pandas, and AWS Glue. • Developed API-based ML inference services, enabling modular execution of predictive models across enterprise systems. • Built structured data processing pipelines for large-scale financial datasets, similar to RAG-style ingestion workflows. • Implemented observability and monitoring systems using Splunk and Grafana, enabling traceability across distributed services. • Integrated CI/CD pipelines (Jenkins, Git) for automated testing and deployment of ML and backend services. • Designed secure microservices architecture on AWS (Lambda, EC2, S3, RDS) with scalable deployments using Docker and Kubernetes. • Built automated validation frameworks using PyTest, ensuring reliability of ML and API systems. • Created data models and analytics layers using SQL and Python, supporting business intelligence and reporting. • Designed and implemented product catalog and service onboarding modules using Django REST Framework and React.js, similar to eCommerce product listings. • Developed secure, role-based Streamlit applications for HR systems with Snowflake backend integration. • Optimized Snowflake queries for HR data retrieval and implemented RBAC controls for sensitive columns. • Developed a secure Streamlit-based web application to manage enterprise HR talent planning data, with a focus on usability, authentication, and governance. • Integrated Snowflake for backend storage, implementing row/column-level access controls and role-based data models aligned with enterprise HR security policies.

• Designed and deployed microservice-based ML systems using Python and Golang, supporting scalable financial applications. • Built ML models (CNNs, RNNs, NLP models) using TensorFlow and PyTorch for classification and forecasting tasks. • Developed API orchestration layers using Flask and Django, enabling modular ML service integration. • Implemented data pipelines using PySpark and AWS Glue, supporting structured data ingestion and transformation. • Designed event-driven architectures using Kafka-like messaging patterns for real-time data processing • Built ML evaluation workflows using Python and statistical metrics for model validation and performance tracking. • Implemented observability using Splunk-based logging pipelines, enabling system-wide monitoring and debugging. • Worked on cloud-based deployment of ML services using AWS (Lambda, EC2, RDS, Redshift). • Built automated CI/CD pipelines using Jenkins and Docker, improving deployment reliability. • Developed data transformation workflows for structured/unstructured datasets, supporting analytics and reporting systems. • Integrated Redux for centralized state management to optimize user interactions and UI performance. • Built asynchronous APIs using FastAPI and Pydantic for input validation, resulting in highly efficient backend services. • Designed and optimized MongoDB schemas, improving query performance and database scalability. • Developed asynchronous services in Python using asyncio and optimized concurrent tasks with aiohttp. • Designed and developed RESTful APIs for data manipulation and integration with cross-functional systems.

• Built data-driven ML models for healthcare analytics and predictive insights using Scikit-learn. • Developed ETL pipelines using PySpark and Hadoop for large-scale healthcare data processing. • Implemented data ingestion and transformation workflows from multiple sources into structured datasets. • Built API services for ML model integration using Python and Django REST framework. • Worked on big data processing pipelines using Spark for healthcare analytics use cases. • Designed data validation and quality frameworks for clinical datasets. • Contributed to distributed data processing systems supporting analytics and reporting platforms. • Implemented AI/ML models to analyze patient data trends and optimize predictive healthcare analytics. ● Built interactive and responsive UIs using ReactJs and TypeScript, implementing state management with Zustand for lightweight, efficient performance. ● Developed backend APIs with FastAPI and integrated Pydantic for data validation and model consistency. ● Designed and managed MongoDB databases, implementing queries optimized for high-throughput applications. ● Implemented asynchronous programming using Python's asyncio for improved backend performance under concurrent workloads. ● Wrote unit and integration tests with Pytest and Jest to ensure robust application functionality. ● Deployed CI/CD pipelines for continuous testing and automated deployment, reducing delivery time and improving reliability. ● Ensured cross-browser compatibility and responsive design for all UI components. ● Developed Shell scripts for cleansing and validating data files using utilities like AWK, grep and other UNIX commands. ● Designed and implemented full-stack applications using Vue.js for the frontend and Flask for the backend, optimizing performance and reliability. ● Developed Shell scripts implementing PL/SQL queries for data migration & batch processing.

● Analysed various cross-functional, multi-platform applications systems enforcing Python best practices and providing guidance in making long term architectural design decisions. ● Coded model level validation using Python programming with emphasis in web security. ● Handled Business logics by backend Python programming to achieve optimal results. ● Implemented jQuery to improve the overall usability of the website interface and AJAX functionality to speed up web application. ● Developed views and templates in Django to create a user-friendly website interface. ● Configured Django to manage URLs and application parameters. ● Extensively used Python / Django Framework for developing backend of web applications. ● Used Django APIs for database access. ● Developed web services using Restful technology to support JSON and XML. ● Created new connections through applications for better access to MySQL database and involved in writing SQL & PL SQL - Stored procedures, functions, sequences, triggers, cursors, object types etc. ● Built and deployed ML models for fraud detection and predictive analytics, utilizing TensorFlow and GCP for scalable performance. ● Designed RabbitMQ/MongoDB pipeline for routing/storing of email bounce handling records. ● Developed and tested many features in an Agile environment using Python, Django, HTML5, CSS, JavaScript, Bootstrap. ● Created dynamic frontend features using Angular, integrated with Django-based backend APIs for seamless data interaction. ● Developed Capistrano scripts to automate deployment on various environments. ● Used GIT as Source control repositories with regular code reviews and pull/merge requests.

• Designed and implemented database objects, including tables, views, triggers, and stored procedures, to support application requirements. • Developed applications using Oracle tools like SQL Developer and SQL*Loader for ETL and data management operations. • Created Python-based ETL scripts using Celery and Celery Beat for scheduling and batch processing. • Built Python/Django web applications with PostgreSQL and MySQL, integrating APIs for email, messaging, and storage services. • Designed and implemented front-end architectures using React, Redux, and ES6 for dynamic and responsive user interfaces. • Automated test scripts using Selenium WebDriver and integrated them into CI/CD pipelines with Jenkins. • Migrated customer data from legacy platforms to AWS in a cloud-based integration platform (iPaaS). • Utilized NumPy and SQLAlchemy for automating performance calculations and improving data analysis workflows. • Optimized SQL queries and stored procedures for enhanced application performance and efficiency. • Developed backend frameworks using Flask and Django for RESTful APIs and microservices architecture. • Created dynamic GUIs using Webapp2 to display test block documentation and Python code features in a browser. • Automated batch jobs for database table loading and implemented ETL processes to streamline data integration. • Integrated Python-based solutions with third-party APIs for seamless data sharing and communication. • Resolved defects and implemented new features, ensuring the reliability and scalability of applications.