Member Spotlight: Sairohith Thummarakoti on Serverless Edge Intelligence and Agentic AI

By IEEE Computer Society Team on

July 12, 2026

Meet Sairohith Thummarakoti, a Pega Cloud Lead System Architect specializing in the design and modernization of large-scale, mission-critical digital systems in healthcare and public safety environments. With a prolific background featuring four books and more than 30 research papers, his work resides at the cutting edge of distributed infrastructure and automated workflows.

Please introduce yourself and briefly describe your work.

I am a Pega Cloud Lead System Architect specializing in the design and modernization of large-scale, mission-critical digital systems in healthcare and public safety environments. My work focuses on building resilient cloud architectures that support complex, high-volume operations with strong requirements around reliability, security, and continuity of care.

I work at the intersection of cloud computing, AI-driven automation, and low-code platforms to design systems that can scale across large, distributed networks while maintaining operational stability. My focus is on ensuring that critical healthcare and enterprise systems remain continuously available and capable of handling real-time workloads without disruption.

You have published four books with Taylor & Francis and more than 30 research papers. What are you working on next, and how do you see this project contributing to AI, cloud computing, or digital transformation?

My earlier publications focused on scalable cloud architectures and intelligent enterprise systems, with an emphasis on modernization across healthcare and regulated industries. My upcoming work with Elsevier, Serverless Edge Intelligence: Architectures, Platforms, and Maturity Models Across the Edge-Cloud Continuum, extends this foundation into edge-native and serverless-driven architectures.

This book explores how enterprises can move beyond traditional cloud models toward distributed, intelligent systems capable of handling real-time workloads with improved responsiveness and resilience. It also addresses the architectural and compliance requirements of industries such as healthcare and finance, where performance must be balanced with strict regulatory controls.

As Chair of the IEEE Computer Society Columbia Section and a selected speaker for the Early Career Speaker Program (ECSP), leadership appears to be an important part of your career. What has been the most rewarding part of mentoring and inspiring early-career professionals and students within the IEEE community?

My earlier publications focused on scalable cloud architecture and intelligent enterprise systems, with an emphasis on modernization across healthcare and other regulated industries. My upcoming work with Elsevier, Serverless Edge Intelligence: Architectures, Platforms, and Maturity Models Across the Edge-Cloud Continuum, extends this foundation into edge-native and serverless-driven architectures.

Alongside my research and publications, I am also participating in IEEE’s “adopt a student” initiative, where I mentor and guide a selected student by supporting their technical growth, professional development, and engagement with the broader engineering community.

Your expertise spans AI, cloud computing, and low-code/no-code platforms. In your view, how are low-code/no-code tools changing digital transformation? What developments in this area are you most excited about right now?

Low-code and no-code tools are no longer just for building basic apps; they are completely changing how fast companies can modernize. By turning complex coding into simple visual blocks, they let businesses update their workflows quickly without getting bogged down by complicated IT infrastructure. What excites me most right now is combining Generative AI with advanced low-code platforms like Pega Infinity. We are entering an era where AI can instantly read complex business rules, automatically build the software, and launch it in days instead of months—all while keeping the systems incredibly safe, secure, and reliable.

You speak at both industry conferences and academic events, giving you a broad perspective. From your experience, how do the priorities of industry and academia differ when advancing AI and cloud computing, and how do you balance those perspectives in your work?

Academia and industry approach AI and cloud computing from different perspectives. Academia focuses on advancing knowledge through research and experimentation, while industry prioritizes building secure, scalable, reliable, and compliant solutions that deliver measurable business value. A promising AI model must ultimately prove it can perform reliably in real-world, enterprise environments.

As both a practicing enterprise architect and an author, I bridge these two worlds by translating research into enterprise-grade solutions for healthcare and financial organizations while using real-world challenges to shape future research. This creates a continuous exchange between theory and practice.

My role on Industry Advisory Boards for multiple public universities further strengthens this connection. I work with faculty to align engineering curricula with evolving industry needs by sharing real-world case studies, enterprise architectures, and emerging AI trends. Through my technical leadership, publications, and conference presentations, I strive to ensure that academic research remains practical and that innovation continues to benefit both industry and the next generation of engineers.

Looking back, which technical contribution across healthcare and financial systems has had the greatest impact in your career, and why?

Looking back, the technical contribution that has had the greatest impact is the enterprise cloud architecture I designed to modernize an oncology care network spanning more than 180 healthcare facilities. I led the design of a secure, highly available cloud platform that now supports over 130,000 patients annually while ensuring uninterrupted clinical operations.

A key innovation was integrating Agentic AI into the enterprise workflow to automate the processing and prioritization of approximately 1.75 million pathology reports each year. This AI-driven approach significantly reduced administrative delays and improved nurse navigator productivity by 35%, enabling faster coordination of patient care.

This contribution reflects my approach to engineering: combining cloud computing, AI, and enterprise architecture to solve complex, real-world challenges. Transforming advanced research into secure, scalable, and impactful healthcare solutions has been the most rewarding and meaningful contribution of my career.

Your work has been recognized through international awards, invited speaking engagements, technical publications, and leadership roles within IEEE. How have these experiences shaped your vision for the future of enterprise AI and cloud computing, and what legacy do you hope your work will leave?

My vision for enterprise AI and cloud computing is focused on building systems that are tightly integrated, secure, and execution driven. The future lies in combining Agentic AI for reasoning, cloud platforms for scalability, and low-code systems for rapid delivery, while ensuring everything operates under strict governance, compliance, and deterministic guardrails rather than unpredictable outputs.

Looking ahead, I aim to leave behind two contributions: architectural blueprints for high-stakes industries such as healthcare, oncology care systems, and other critical enterprise environments, which can serve as reliable reference models for secure and scalable digital transformation. I also aim to build an educational legacy through my books, IEEE leadership, and advisory roles that helps shape the next generation of system architects capable of designing responsible, secure, and scalable AI-driven infrastructure.

Keep in Touch with Sairohith

Readers can learn more about my work or contact me via IEEE email, LinkedIn, or Google Scholar.