Jaya Thondepu

Python Full Stack Engineer with 4+ years of experience designing and developing scalable web applications, backend microservices, and AI-enabled solutions across wealth management, financial services, payments, risk analytics, and e-commerce domains. Strong experience building full-stack applications using Python (FastAPI, Django, Flask), React.js, TypeScript, PostgreSQL, and RESTful APIs, with hands-on expertise in microservices, event-driven architectures, and cloud-native development on Azure and AWS. Experienced in applying machine learning and AI techniques, including scikit-learn, XGBoost, NLP, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Elasticsearch/OpenSearch, and MLflow, to support fraud detection, compliance automation, document intelligence, and intelligent search solutions.

Proficient in developing secure, scalable applications using Docker, Kubernetes, Apache Kafka, Redis, Jenkins, and CI/CD pipelines while collaborating within Agile teams to deliver high-quality enterprise software.

Python Full Stack Engineer at Raymond James (2025-08 – Present)

Developed full-stack applications using Python (FastAPI, Django) and React.js to support wealth management, brokerage operations, compliance, and advisor servicing platforms.

• Designed and developed secure REST APIs and backend microservices for client onboarding, account servicing, compliance workflows, and regulatory reporting, reducing API response times by nearly 25% through query optimization and asynchronous processing.
• Built responsive React.js dashboards using TypeScript, React Hooks, and Material UI for advisor insights, compliance alerts, and operational reporting.
• Developed Python data processing pipelines using Pandas, NumPy, and SQL to integrate client, transaction, portfolio, and audit data from enterprise systems.
• Designed and optimized PostgreSQL databases using SQLAlchemy, improving query performance and supporting high-volume financial reporting.
• Implemented Apache Kafka for event-driven processing of account activities, compliance notifications, and audit events across distributed services.
• Integrated RAG and LLM capabilities with Elasticsearch/OpenSearch to enable internal users to search policies, procedures, and regulatory documentation using natural language, reducing document search time for compliance teams by approximately 35%.
• Built NLP-based document classification and information extraction solutions using spaCy and scikit-learn to automate compliance document processing.
• Developed and evaluated machine learning models using scikit-learn and XGBoost to support fraud detection and transaction risk assessment as part of compliance initiatives.
• Automated KYC and client document extraction using Azure AI Document Intelligence, reducing manual document processing effort by nearly 40%.
• Managed ML experiments and model versioning using MLflow to support reproducible model development and deployment.
• Containerized applications using Docker and deployed services on Azure Kubernetes Service (AKS) while utilizing Azure Blob Storage, Azure Functions, and Azure Monitor for cloud operations.
• Developed automated unit and integration tests using PyTest and contributed to CI/CD pipelines with Jenkins, Git, and Azure DevOps.
• Worked closely with product owners, business analysts, compliance teams, and QA in an Agile Scrum environment to deliver secure, scalable, and regulatory-compliant solutions.

Software Development Engineer at Paypal (2021-09 – 2023-06)

Built and maintained scalable backend microservices using Python (Django, Flask, FastAPI) for payment processing, transaction monitoring, and risk platforms handling millions of transactions annually.

• Designed event-driven REST APIs using FastAPI and Apache Kafka to support payment authorization, fraud scoring, and real-time transaction monitoring across distributed systems.
• Developed and deployed machine learning models using scikit-learn and XGBoost for fraud and anomaly detection, owning feature engineering, model evaluation, performance monitoring, and explainability to improve prediction reliability.
• Integrated transaction, device, behavioral, and customer signals into a real-time fraud-detection platform, reducing false positives by approximately 20% and improving risk-decisioning workflows.
• Applied NLP and text-classification techniques to transaction descriptions and customer-support interactions to identify risk patterns, improve case prioritization, and generate operational insights.
• Built large-scale data ingestion and processing pipelines using Pandas, NumPy, SQL, and Apache Kafka, improving data-processing efficiency by approximately 30%.
• Optimized PostgreSQL schemas, indexing strategies, and SQL query performance for high-volume payment and analytics workloads, improving query execution times by approximately 25%.
• Implemented Redis-based caching solutions to reduce database load and improve application response times across transaction-processing services.
• Developed reporting and operational monitoring solutions using Python, Pandas, SQL, and visualization libraries to provide insights into payment processing, fraud trends, and risk metrics.
• Built automated testing frameworks using PyTest and unittest, improving application reliability and reducing production defects across critical payment services.
• Containerized applications using Docker and contributed to deployments on AWS (EC2, S3, RDS, Lambda, CloudWatch), supporting highly available and scalable production environments.
• Contributed to CI/CD pipelines using Jenkins, Git, and automated deployment processes, improving release consistency and deployment efficiency.
• Collaborated with product managers, data scientists, risk analysts, QA engineers, and DevOps teams to deliver secure, scalable, and high-performance payment and risk-management solutions within Agile/Scrum environments.

Python Developer at Epam (2020-06 – 2021-08)

Worked as part of an Agile delivery team supporting a US-based retail client, developing customer-facing e-commerce applications for product catalog, shopping cart, checkout, order management, and payment workflows.

• Developed full-stack features using Python (Django, Flask), React.js, JavaScript, HTML5, and CSS3, translating business requirements and user stories into scalable solutions.
• Built responsive React.js components and optimized front-end performance, reducing page load times by approximately 20% across key shopping experiences.
• Designed and developed RESTful APIs to support inventory management, order processing, payment integrations, and customer account services.
• Built FastAPI-based microservices with asynchronous processing to support near real-time order status updates and backend operations.
• Implemented background processing and scheduled jobs using Celery and Redis for order confirmations, email notifications, and reporting workflows.
• Utilized Apache Kafka for event-driven processing of order, inventory, and fulfillment events, improving scalability and service decoupling.
• Developed and optimized database solutions using PostgreSQL and MySQL, leveraging Django ORM, SQLAlchemy, query tuning, and indexing strategies that improved query performance by approximately 25%.
• Implemented secure authentication and authorization using OAuth2, JWT, and role-based access control (RBAC) while following secure coding practices.
• Containerized applications using Docker and collaborated with DevOps teams to deploy services on AWS (EC2, S3, RDS, Lambda, CloudWatch) and Kubernetes (EKS).
• Developed automated unit and integration tests using PyTest and unittest, improving application reliability and reducing production defects.
• Contributed to CI/CD pipelines using GitHub Actions, documented APIs with Swagger/OpenAPI, and participated in Agile/Scrum ceremonies using JIRA and Confluence.

Master's in Computer Science – University of South Florida