• Developed and deployed GenAI-powered AI agents using LangChain, LangGraph, and LLM APIs to automate financial support, case handling, and internal workflow use cases, reducing manual resolution time by more than 50% and improving consistency across enterprise operations.

• Engineered secure backend APIs and Python-based services to integrate LLM capabilities into enterprise applications, enabling scalable, auditable, and production-ready AI experiences in a regulated financial environment.

• Built RAG pipelines for real-time retrieval of policy, KYC, and transaction-related documents using vector search and custom retrieval strategies, improving retrieval quality and response accuracy in compliance-sensitive workflows.

• Designed and implemented multi-agent orchestration workflows across planning, retrieval, and validation steps to automate complex business processes, helping improve execution speed for multi-step financial operations by 30% while maintaining transparency and control.

• Established LLM evaluation, observability, and validation frameworks using automated metrics, tracing, and monitoring, improving output reliability, reducing prompt regressions, and strengthening production readiness of AI systems.

• Collaborated with product, platform, and engineering teams to design, build, and deploy cloud-native AI software solutions across the full lifecycle, from architecture and backend integration to testing, deployment, and production support.

• Built and integrated an AI-powered workflow using Python and prompt-based processing to automate internal analysis tasks, improving response consistency and reducing manual effort across business operations.

• Developed backend features and workflow logic using Python and SQL, contributing to internal tools that streamlined operational processes and improved efficiency across enterprise use cases.

• Collaborated with engineers to test, debug, and validate cloud-based software components, helping improve application reliability, deployment readiness, and overall platform stability.

• Assisted in developing and testing machine learning models for research projects, focusing on data preprocessing, feature engineering, and model evaluation.

• Built small-scale Python and scikit-learn pipelines to automate experimentation and improve reproducibility of results.

• Contributed to data visualization and reporting using Matplotlib and Power BI to help faculty and students interpret ML outcomes.

• Collaborated with research teams to integrate ML insights into academic applications, gaining practical exposure to applied AI workflows.

• Developed and maintained backend services for inventory, product availability, and checkout workflows using Java, Spring Boot, and Node.js, enabling reliable processing of high-volume retail transactions across core customer-facing applications.

• Designed and optimized RESTful APIs for product, cart, and order workflows, strengthening service-to-service communication across frontend, backend, and downstream systems while reducing response latency by 25%.

• Built reusable, responsive, and production-ready UI components using React, Redux, and TypeScript, translating business and design requirements into consistent customer-facing experiences across key purchase flows.

• Integrated application modules with internal retail platforms and external vendor systems to support product data flow, availability checks, and order status updates, improving transaction accuracy and streamlining the end-to-end purchase lifecycle.

• Supported release engineering, deployments, and production stability using Jenkins, Docker, AWS, Splunk, and New Relic, helping reduce deployment effort by 30% and improving reliability through monitoring, troubleshooting, and build validation.

• Built and enhanced backend application features using Java, contributing to real-world software development tasks and strengthening core programming skills.

• Applied object-oriented programming principles to develop clean and reusable code, while improving understanding of debugging and application logic.

• Tested and resolved software issues in collaboration with team members, helping improve application functionality and reliability.

• Supported the development of application modules by participating in coding, requirement understanding, and implementation activities.

• Worked closely with mentors and developers in a collaborative environment, gaining hands-on experience in Java development and software engineering practices.