Atul Gupta

Project: Bärcare is complete digital and mobile solution to improve efficiency of outpatient nursing care for caretaker companies by taking care of data collection, documentation and financing.

• Developed microservices for outpatient care using Java, Spring Boot, Kafka, Docker, and Kubernetes in Scrum team of 15 members.
• Designed and implemented high-performance RESTful APIs, resulting in 20% increase in overall system responsiveness.
• Successfully delivered reports, including shift plans and action plans, which led to 30% improvement in operational efficiency.
• Implemented advanced sorting functionality for Invoice PDFs, reducing manual effort for sales team by 70% and resulting in 50% decrease in processing time.
• Mentored new team members on project requirements, coding standards, and best practices.

Developer Ready Platform is cloud-based SaaS enterprise platform which provides software development productivity behaviours for engineers used by 3K+ engineers. The platform is built on microservices architecture (20+ microservices)

• Actively participated as key member of core development team, responsible for requirement gathering, design, and end-to-end implementation of checker microservices.
• Demonstrated expertise in building copyright header validation microservice from scratch, utilising GraphQL and Kafka technologies to evaluate GitHub PR files for compliance in source code.
• Developed robust Rest APIs using Python, PostgreSQL & Django Rest Framework to calculate and report product compliance score. Implemented weighted mean approach to ensure accurate assessment and achieve 20% increase in product quality.
• Took complete ownership of copyright checker module, overseeing all aspects from design to deployment, ensuring smooth and efficient operation throughout entire development lifecycle.

Product: SAP AI Launchpad is multi tenant SaaS application in SAP Business Technology Platform used by 2k+ customers for managing AI use cases.

• Played key role in development and deployment of SAP AI Launchpad across multiple cloud regions.
• Developed comprehensive application from scratch, leveraging SAPUI5 for efficient lifecycle management of ML and AI scenarios, resulting in 50% reduction in manual effort.
• Strengthened overall product security compliance by enabling JWT authentication in Rest APIs, contributing to 30% increase in product security.
• Successfully resolved performance issues and met customer expectations by integrating S3 for artifactory storage, leading to 20% reduction in number of API calls.

Product: SAP AI Core is service in SAP Business Technology Platform to handle execution and operations of AI lifecycle in standardised/scalable way by providing seamless integration with other SAP solutions (with 3000+ customers and 100+ native SAP product adoption).

• Collaborated within agile team of 8 developers (totalling 20+ developers working on project) to enhance backend architecture of project.
• Built highly scalable microservice from scratch using Python, Flask, and HanaDB to manage metrics for machine learning experiments. This microservice featured over 20 REST APIs and effectively handled 10,000+ concurrent API requests, significantly reducing manual workload for more than 30 customers.
• Developed Python SDK that automated AI and ML scenarios in Argo-workflow. This SDK was also utilised by SAP data scientists in Jupyter Notebook for performing ML experiments, resulting in 10% increase in user adoption and usability.