Lakshmi Yogitha Surapaneni

• Built scalable full-stack applications using Python, Django, Flask, React, and PostgreSQL, enabling real-time healthcare data analytics with HIPAA-compliant reporting systems across multiple clinical trial teams. • Designed and optimized ETL pipelines with Pandas, NumPy, and SciPy, improving healthcare data ingestion by 40% and ensuring accuracy across large pharmaceutical datasets. • Migrated monolithic healthcare platforms into AWS microservices using Lambda, Kubernetes, and Terraform, achieving fault tolerance, cost optimization, and improved deployment efficiency. . Integrated AI workflows using prompt engineering, graph-based agents, and LLM orchestration to streamline internal operations. • Implemented secure REST and GraphQL APIs using Django REST Framework and FastAPI, supporting interoperability between internal pharma systems and external regulatory platforms. • Automated deployments through CI/CD pipelines in Jenkins with integrated static code analysis (SonarQube) and unit testing, reducing release cycle times by 35%. • Integrated MongoDB and PostgreSQL databases for storing structured and unstructured healthcare data, enabling seamless queries across diverse data pipelines and compliance systems. • Implemented serverless workflows with AWS Lambda and S3 event triggers, enabling automated healthcare data ingestion and processing pipelines without manual intervention. • Applied Agile and TDD methodologies throughout SDLC phases. • Enhanced healthcare compliance reporting with Flask APIs integrated with OAuth2/JWT, providing secure authentication. • Developed microservices-based architectures, containerized with Docker and Kubernetes, reducing downtime and simplifying version management for healthcare applications. • Built secure data ingestion flows with AWS RDS, S3, and DynamoDB, ensuring backups and reliable data recovery mechanisms for healthcare compliance audits. nd long-term maintainability of applications.

• Developed Python Flask and Django microservices for banking applications, integrated with PostgreSQL and MongoDB to support high-volume transactions and customer-facing services. • Built Kafka-driven ETL pipelines for fraud detection, streaming real-time transaction data and triggering alerts for abnormal banking activity. • Designed React-based dashboards for fraud monitoring and loan analytics, integrating with backend Django APIs for near real-time updates. • Implemented CI/CD pipelines with Jenkins, Maven, and Git, ensuring faster deployments and reducing release failures in banking applications. • Optimized SQL queries and schema in PostgreSQL, improving transaction response times across multiple customer-facing applications. • Migrated key banking systems into AWS Lambda and Kubernetes microservices, improving scalability and system uptime. • Applied Agile and TDD practices across project lifecycle, ensuring code reliability and reducing production defects by 25%. • Configured Terraform scripts for AWS IaC, enabling automated environment provisioning and cost control across development and production systems. • Designed Flask APIs with OAuth2 and JWT for secure customer authentication and third-party integration in banking workflows. • Automated transaction report generation using Pandas, NumPy, and SciPy, allowing better financial trend analysis. • Deployed cloud applications on AWS with RDS, EC2, S3, and CloudFront, ensuring reliability and scalability. • Integrated CI/CD monitoring tools like Prometheus and Grafana, providing proactive system health checks and reducing downtime. • Designed financial compliance workflows using Python, Django, PostgreSQL, aligning with FDIC regulatory requirements. • Collaborated with banking product teams, translating requirements into Python-based microservices, React dashboards, and CI/CD-enabled deployments.

• Designed and developed insurance claims management systems using Django, Flask, and React, automating claims workflows and improving turnaround time by 35% for policyholders. • Optimized PostgreSQL and MongoDB queries for insurance data pipelines, reducing latency in retrieving claims and policy records across distributed systems. • Implemented secure REST APIs with Flask and Django REST Framework, enabling real-time integration with third-party insurance partners and internal applications. • Built scalable microservices architecture using Python, Docker, and Kubernetes, ensuring insurance applications could handle peak loads during claims season. • Automated ETL workflows with Pandas, NumPy, and SciPy, transforming insurance datasets into structured reports for regulatory compliance and executive dashboards. • Developed customer-facing React portals integrated with Django backends, enhancing user experience for policy management and claims submission. • Applied Agile Scrum and TDD methodologies throughout the SDLC, improving software quality and reducing production bugs by 25%. • Migrated legacy insurance applications into AWS Lambda-based serverless architecture, reducing infrastructure costs and improving scalability for seasonal workloads. • Implemented OAuth2 and JWT-based authentication for insurance portals, securing sensitive policyholder data in compliance with industry regulations. • Built Kafka-based messaging pipelines for real-time claims status updates, ensuring customers received near-instant notifications. • Built statistical models using SciPy to identify fraudulent claim patterns, helping reduce false claims by 12%. • Implemented data pipelines with Kafka and PostgreSQL, enabling seamless flow of insurance transactions into reporting systems. • Developed automation scripts in Bash and Python to streamline claims processing, reducing manual interventions in back-office operations.

• Built Python-based automation scripts and ETL pipelines to migrate large-scale client datasets from legacy systems into PostgreSQL and MongoDB with improved reliability. • Designed Flask microservices and REST APIs for client automation projects, improving application modularity and enabling smooth integrations with external systems. • Automated infrastructure provisioning on AWS using Terraform and Jenkins pipelines, ensuring consistent environments and reducing setup times by 40%. • Developed Django-based web applications with secure API endpoints, enabling clients to access operational dashboards and reporting services. • Implemented CI/CD pipelines with Jenkins for continuous testing and deployments, reducing release cycles and improving team productivity. • Built Kafka-powered data pipelines for high-volume client transactions, enabling real-time ingestion and reporting across multiple business units. • Configured OAuth2/JWT authentication mechanisms in Python APIs to ensure secure client integrations. • Migrated on-prem legacy workflows into AWS Lambda functions and S3-based pipelines, reducing infrastructure overhead and manual operations. • Designed PostgreSQL data models with optimized schemas for handling migrated datasets, improving query performance. • Enhanced reporting workflows by designing Flask APIs to serve structured migration reports to client stakeholders. • Applied full SDLC practices from requirement analysis to deployment, supporting multiple concurrent client engagements.