Pranathi Depa

Led front-end development for AI-driven enterprise applications using React.js, delivering scalable, responsive UIs for analytics and automation. Architected and implemented LLM-powered conversational interfaces with multi-turn interactions and streaming responses. Built document intelligence UIs supporting file ingestion, preprocessing, and structured visualization of AI outputs. Developed dashboards to translate complex ML results into actionable business insights. Integrated REST and LLM APIs to enable intelligent workflows and real-time data experiences, collaborating with ML engineers to convert model outputs into intuitive interfaces. Implemented state management with Redux Toolkit and Context API; built reusable UI components and design systems; created secure authentication flows using OAuth2, OpenID Connect, Okta, Azure AD, and Ping Identity. Built multi-tenant RBAC-enabled UI, and implemented form-driven workflows for ARGUS case intake and AI-assisted review processes. Led de

Continuing to lead hands-on front-end development with React.js for enterprise AI platforms, building scalable, production-ready applications for US-based teams. Developed and enhanced front-end features for real-time AI and analytics use cases, focusing on performance, maintainability, and security. Architected reusable components and shared UI patterns, implemented Redux Toolkit and Context API for complex state management, and designed RBAC-based UI access controls. Optimized performance via lazy loading, code splitting, memoization, and caching strategies. Integrated backend services (REST and microservices) and LLM APIs to support intelligent workflows, while contributing to CI/CD pipelines and cloud deployments with Docker, Kubernetes, AWS, and GCP. Mentored developers on architecture, performance tuning, and best practices for enterprise front-end development.

Led development of AI-driven enterprise applications, delivering scalable, real-time platforms used for advanced analytics and intelligent automation. Architected and delivered LLM-powered conversational applications with multi-turn interactions and streaming responses to enhance user engagement. Built document intelligence systems supporting file ingestion, preprocessing, and visualization of AI-generated outputs for business users. Designed and developed analytics dashboards that translate complex ML outputs into actionable insights for decision-makers. Integrated LLM APIs and AI services into production workflows, enabling automation and intelligent data processing across applications. Collaborated with ML engineers and data scientists to translate model outputs into intuitive user experiences, improving interpretability and adoption. Developed AI-driven workflows to automate extraction of case data from inbound formats such as E2B, reducing manual intervention. Designed systems to