Srividya Rampally

• Architected and deployed scalable AI-powered enterprise applications for Newell CSI and Design teams using Python, Fast API, React, TypeScript, and cloud-native microservices, enabling intelligent workflow automation and AI-driven business insights across enterprise operations.
• Designed and implemented end-to-end agentic AI solutions leveraging multi-agent orchestration, MCP-based architectures, autonomous AI workflows, LLM integrations, RAG pipelines, vector search, and semantic retrieval to improve operational efficiency, intelligent knowledge discovery, and enterprise decision-making.
• Built enterprise-grade AI platforms utilizing Azure AI Foundry, Azure Cognitive Services, Azure AI Vision Services, Azure Data bricks, PostgreSQL, and containerized Azure infrastructure to deliver scalable document intelligence, intelligent retrieval, semantic search, and AI-powered analytics solutions.
• Led cloud deployment strategies, CI/CD automation, and release management using Azure DevOps and containerized environments, improving scalability, deployment reliability, operational efficiency, and production readiness of enterprise AI applications.
• Collaborated with cross-functional business, Insights, and Design teams to gather requirements, translate complex enterprise workflows into scalable AI solutions, and drive successful enterprise AI adoption and modernization initiatives.
• Applied modern AI engineering practices including enterprise AI integration, prompt engineering, cloud-native architectures, intelligent automation, scalable microservices, and production-grade AI deployment frameworks to deliver high-impact AI solutions in enterprise environments.

• Architected and implemented a comprehensive development lifecycle for scalable mobile and web applications.
• Developed AI-enabled enhancements for Relay mobile applications supporting intelligent logistics workflows, real-time operational insights, and scalable driver-assistance capabilities across Amazon Freight operations.
• Collaborated on scalable backend and mobile application integrations leveraging AWS AI/ML services to improve operational efficiency, delivery intelligence, and real-time logistics decision-making.
• Engineered cross-platform Android/iOS applications utilizing React Native, enhancing real-time package tracking and delivery updates, which improved driver routing efficiency by 13%.
• Configured high-performance APIs using Java Spring Boot and AWS services to support real-time logistics operations.
• Delivered configuration management tools to track server settings for performance testing, which saved 25% of the initial machine set-up.
• Sphearheaded the creation of a microservice architecture using Spring Boot, FastAPI, and Node.js, which served as a foundation for three new products launched later that year.
• Navigated the transition to AWS CodePipeline, structuring CI/CD pipelines that included automated testing and rollback capabilities, decreasing deployment failures by 30%.
• Pioneered RESTful APIs using AWS Lambda and API Gateway, as per business requirements for new features.
• Proposed a Gen AI-powered code review tool, decreasing code defects by 8% and enhancing team efficiency by 10% through automated feedback and suggestions.
• Led the transition to a fully Agile development environment, using Jira for task management, resulting in a 12% improvement in project delivery speed and team responsiveness.

Technical Environment: Python , AWS API & Lambda, Fast API, Postman, React Native, Node.js, Cloud watch, Cloud Formation, AWS EC2, Android SDK, REST, Py charm, VS Code, Intelli J, Android Studio, GitHub, AWS Kinesis, AWS RDS,, Gen AI, Machine Learning.

• Collaborated with engineering teams for gathering requirements, system analysis, design, development, testing, and deployment.
• Formulated reusable software components in three different projects, that reduced development effort by 50%.
• Mechanized a data cleansing processes using Python scripts and SQL queries led to the discovery of over 500 duplicate records and a significant improvement in asset data integrity for reporting.
• Refined API performance by 60% by fine-tuning database queries and implementing Redis caching.
• Outlined RESTful APIs and integrated SQL databases, ensuring high availability and low-latency data retrieval.

Technical Environment: Java, CSS, JavaScript, TypeScript, SQL, NoSQL Databases.

• Fortified backend architecture for three critical web applications, leveraging Python and Django; this reduced server latency by 22%.
• Redesigned a few APIs to facilitate seamless communication between front-end and back-end systems, improving the overall user experience.
• Reorganized database queries, built caching strategies, resulting in a 30% reduction in response time, and revamped overall efficiency.
• Facilitated stakeholder alignment meetings to gather requirements, which resulted in a 20% improvement in project scope accuracy, and an 80% on-time project delivery rate for all projects.

Technical Environment: C, Python, Spring Boot, Spring REST, MySQL, and Relational databases, MongoDB.