Rajesh Soma
NetApp (United States), HCL Technologies (United States), University of the Cumberlands, Jawaharlal Nehru Technological University, Hyderabad, HCL Technologies (India)
0009-0002-5436-4777
About
Rajesh Soma is an artificial intelligence researcher and enterprise technology professional with more than 14 years of experience in designing, integrating, and delivering large-scale enterprise and cloud solutions. He earned his Master of Science in Artificial Intelligence from the University of the Cumberlands, USA, in 2026. His academic and professional work focuses on applying artificial intelligence, large language models, agentic systems, cloud computing, and intelligent automation to complex enterprise environments.
His professional expertise spans enterprise application architecture, Configure-Price-Quote (CPQ), Quote-to-Order (Q2O), subscription and billing processes, business process automation, cloud integration, and AI-enabled enterprise systems. He has extensive hands-on experience with Oracle CPQ, Oracle Fusion Cloud, Oracle Integration Cloud, Salesforce, enterprise APIs, cloud databases, low-code integration platforms, and AI-driven automation technologies. Throughout his career, he has worked across development, architecture, integration, and business systems roles, translating complex business requirements into scalable and reliable enterprise solutions.
His research interests include artificial intelligence for business process automation, large language model integration with enterprise applications, agentic AI, intelligent CPQ and Q2O systems, retrieval-augmented generation, enterprise knowledge systems, cloud architecture and optimization, federated learning, edge computing, AI-assisted decision support, contract risk automation, low-code automation frameworks, and digital transformation.
Soma has authored and co-authored peer-reviewed research addressing artificial intelligence, enterprise automation, large language models, intelligent enterprise systems, federated learning, edge computing, and cloud-based business platforms. His scholarly work includes publications and accepted research in international journals and IEEE venues, including IEEE Access and Scientific Reports, as well as research presented at international conferences. His work on federated learning for multi-parameter optimization in edge computing received a Best Paper Award at IEEE ISED 2025, and his research on automated printing infrastructure received a Best Paper Award at IEEE DICCT 2026.
In addition to his publication record, Soma actively contributes to the scholarly community through peer review, conference service, technical committees, editorial activities, judging, and session-chair responsibilities. He has reviewed manuscripts for Scientific Reports, IEEE-related journals and conferences, and other international research venues. He has also served on scientific and technical committees and chaired conference sessions in areas related to artificial intelligence, machine learning, communications, and emerging computing technologies. These activities reflect his continuing commitment to research quality, academic service, and knowledge dissemination.
Soma is an IEEE Senior Member and a member of the IEEE Computer Society, Sigma Xi, and the Association for Computing Machinery (ACM). He has also contributed to applied research and innovation through work on intelligent enterprise platforms, AI-driven business systems, and patent-related technology development.
A central theme of his work is bridging academic research with real-world enterprise implementation. His research emphasizes solutions that are not only technically innovative but also practical, scalable, secure, and applicable to large organizations. He is particularly interested in how large language models and autonomous agents can be integrated with enterprise platforms while maintaining reliability, governance, interoperability, and operational efficiency.
His long-term research objective is to advance intelligent enterprise architectures that combine artificial intelligence, agentic systems, large language models, cloud platforms, enterprise integration, and automation technologies. Through both scholarly research and industry practice, he aims to contribute to the development of trustworthy and scalable AI systems that improve enterprise decision-making, process efficiency, knowledge access, and digital transformation.
Employment
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NetApp (United States) Business Systems Analyst2021 - Present
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HCL Technologies (United States) Technical Lead2020 - 2021
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HCL Technologies (India) Technical Lead2017 - 2020
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Deloitte (United States) Technical Analyst2014 - 2017
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Tech Mahindra (India) Software Engineer2012 - 2014
Education
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University of the Cumberlands Master of Science in Artificial Intelligence2025 - 2026
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Jawaharlal Nehru Technological University, Hyderabad Bachelor's2007 - 2011
Projects & Funding
Projects & funding information is unavailable.
Publications (6)
- A Novel Hybrid Edge-Cloud Quantum Support Vector Machine Architecture for Dual-Level Risk Assessment of IoT-Enabled Cloud Applications Save
- A Novel Generative AI-based Framework for Anomaly Detection in the UAVs Networks Save
- End-to-End Architecture for LLM-Based Business Process Automation: A Low-Code Integration Framework With Performance Benchmarking Save
- FedCPQ: Policy-Constrained Federated Reinforcement Learning for Multi-Tenant Configure-Price-Quote Systems,” for publication in Expert Systems with Applications Save
- A Federated Learning Framework for Multi-Parameter Optimization in Edge Computing Save
- AI Business Process Automation Performance Dataset: Task Completion, Benchmarking, and Low-Code Integration Save