• Led the transition from a legacy monolithic system to microservices, defining service boundaries and implementing Python and Go based microservices to modernize outdated infrastructure.• Implemented comprehensive monitoring with Datadog, integrating traces, logs, metrics, and defining critical alerts and SLAs to enhance service reliability and visibility.• Migrated services from barebones servers to Docker, resolving environment inconsistencies, and deploying them on Kubernetes clusters using AWS CloudFormation.• Enabled Infrastructure as Code (IaC) with Terraform, empowering developers to manage configurations more efficiently, and automated CI/CD pipelines using Jenkins.• Improved system performance by optimizing internal service calls with asynchronous processing and multithreading using Python Async and Go Routines, reducing response times.• Collaborated with the architecture team to decouple business areas from the monolithic system, creating scalable microservices.• Optimized SQL schemas and led data migrations using PySpark for faster access and improved database efficiency.• Initiated Lunch and Learn sessions to promote cross-team collaboration and knowledge sharing on new technologies and migration best practices.• Led a backend architecture group, conducting bi-weekly sessions to address migration challenges and guide teams on adopting Docker, Kubernetes, and microservices.• Collaborated with Front-End teams using React.js and Node.js to unblock development tasks, speeding up workflows and enabling smoother cross-team operations.

● Built services and jobs using Python, SQL and microservices to integrate data and predictive algorithms, enabling users to compare financial products.● Aggregated deal and funding data from multiple sources into a structured database, generating insights for customers.● Utilized REST APIs, implemented Swagger for comprehensive documentation, and led UAT to ensure data accuracy and product reliability.

• Migrated on-premise services and databases to AWS using Python, S3, and RDS, reducing operational costs by $21k per month. • Changed microservice deployment from virtual environments to docker containers using pre-build Jenkins libraries reducing pipeline time from 251s to 104s. • Implemented Data Archival Strategy wherein jobs archived historical transaction data while maintaining data required for systems current usage. • Enabled Infrastructure as a Code (IaaC/IaC) in Terraform translating to required AWS and GCP Infrastructure during deployment Jenkins pipeline. • Involved in setting up agents on the server to collect data and installed and configured the Datadog agent on the Linux server.• Orchestrated warm Docker containers to enable efficient application containerization and rapid deployment.• Managed IAM policies for organizations in AWS to define groups, create users, assign roles, and define rules for role-based access to AWS resources, created S3 buckets, and managed policies for S3 buckets.• Wrote Bash, Python Scripts, and Ansible playbooks for infrastructure Automation and Deployment process. • Automated workflow and optimized seamless operation of build jobs and established continuous integration and deployment through the configuration of various plug-ins in Jenkins.• Utilized PyUnit, the Python Unit test framework, for all Python applications and used Django Database API's to access database objects.

● Built Full Stack health insurance premiums application utilizing Java Spring Boot,JavaScript and TypeScript in conjunction with React and AngularJS.● Integrated third-party financial services APIs, including payment gateways andcredit score providers, leveraging TypeScript's strong typing for error prevention.● Played a key role in the migration of legacy systems to a cloud-basedinfrastructure, resulting in improved scalability and cost-efficiency.● Developed complex SQL queries and stored procedures to generate financialreports, perform data analytics, and support regulatory compliance.● Created TypeScript-based data analytics and ETL scripts using AWS Glue, PySpark,and GCP Dataflow to report financial Key Performance Indicators (KPIs) in real-time.● Applied machine learning techniques using TensorFlow/PyTorch to build models for predictive analytics, risk assessment, and financial forecasting.

• Developed user interface guidelines and standards for website development using HTML5, CSS, JavaScript, and jQuery, ensuring consistency and responsiveness across multiple platforms.• Implemented service backends utilizing Java, Python, Node.js, and SQL to support robust data-driven web applications and seamless integration with front-end components.• Optimized database queries and backend workflows, improving the website's overall performance and reducing load times by 20%.• Collaborated with cross-functional teams to ensure smooth integration of front-end interfaces with backend services, enhancing user experience and operational efficiency.• Worked in Agile-Scrum and TDD environments with distinct roles. • Used Python and Django for creating graphics, XML processing of documents, data exchange, and business logic implementation between servers.• Supported Apache Tomcat Web server on Linux Platform.• Developed and executed the User Acceptance Testing portion of the test plan. • Involved in writing application-level code to interact with APIs and web Serving using AJAX, JSON, and XML.Environment: Node.js, Java, Python, JavaScript, jQuery, SQL.