Padmini K

•Designed and deployed generative AI chatbots using AWS Bedrock and models like Anthropic Claude and Amazon Titan, enabling intelligent, personalized user interactions. •Developed RAG pipelines by integrating LangChain and vector databases to provide contextually relevant answers from enterprise documents. •Built serverless, event-driven microservices in Python using AWS Lambda, API Gateway, SQS, and DynamoDB Streams for scalable backend processing. •Implemented Bedrock Knowledge Bases to dynamically ground chatbot responses with private datasets stored in Amazon S3 and OpenSearch. •Built SPA UIs for chatbot interfaces using React.js, enabling real-time interactions via WebSocket and AWS backend integration. •Utilized Python libraries such as BeautifulSoup, Requests, and Selenium for automated data extraction and input generation for LLMs.

• Aligned Faros documentation and SSO configurations with organizational standards, ensuring robust access management and security. • Streamlined data integration by collaborating with ServiceNow and Aion teams, while optimizing Snowflake queries for performance and cost. • Automated workflows using Python, boosting efficiency by 40%, and implemented CI/CD pipelines with Jenkins for seamless deployment. • Designed scalable storage solutions by replicating S3 buckets across regions and configuring IAM roles to enforce secure, role-based access.

• Built Single Page Applications (SPA), Responsive Web Design (RWD) UI, Rich Restful Service Applica-tions, using Angular14. • Integrated front-end interfaces using HTML, CSS, JavaScript, and TypeScript to enhance user usability and user accessibility. • Utilized data science libraries such as Pandas, NumPy, and Matplotlib to analyze and visualize datasets, supporting predictive analytics and reporting. • Collaborated with database administrators to optimize PostgreSQL and MongoDB queries for improved system performance. • Participated in Agile ceremonies, including sprint planning and retrospectives, to improve development effi-ciency and team collaboration.

• Developed web applications using Python, Django, and Flask, ensuring seamless integration between frontend and backend systems. • Implemented RESTful APIs to enable communication between microservices and frontend, improving application scalability and performance. • Managed containerized applications using Docker and orchestrated them with Kubernetes, ensuring high availability and scalability. • Conducted load, unit, and regression testing to ensure application reliability and stability, while optimizing Python GPU/CPU usage for high-performance computing tasks

Participated in intensive training sessions focused on machine learning algorithms, data preprocessing, feature engineering, and model evaluation, under the guidance of industry experts. Innovative knowledge to develop predictive models for estimating the cost of used cars, leveraging techniques such as regression analysis, decision trees, and ensemble methods. Collaborated with a multidisciplinary team to gather and preprocess data, select appropriate features, and fine-tune model parameters to achieve high accuracy and robust performance.

Pasiately influenced in practical exercises and lab sessions to apply theoretical knowledge, gaining proficiency in network setup, troubleshooting, and optimization. Influence at creating dynamic web applications for data processing and visualization, utilizing front-end technologies like HTML, CSS, JavaScript, AngularJS, jQuery, and data visualization tools such as Tableau. Collaborated with peers and instructors to solve real-world networking challenges, enhancing problem-solving abilities and teamwork skills.