Apoorva Kasoju is a Senior Software Development Engineer at Amazon, her work and research is at the intersection of large-scale cloud infrastructure, DevOps, and responsible AI. She has worked on high impact, mission-critical systems for over nine years and is an author of multiple papers on responsible AI. Apoorva is a trusted thought leader in both industry and academia who shares her perspectives and insights on research, publications, building scalable products, navigating the dual demands of innovation and accountability, and mentoring the next generation of software engineers.
In this interview, we talk about her work, how she develops a competitive edge to craft scalable software products, the real world considerations of building AI into these products, as well as some emerging global trends around the adoption and use of real time AI deployments.
You’ve led high-impact projects at Amazon, like the FBA Dashboard and Customer Returns Card. What are the core principles you follow when building systems that need to scale globally and deliver measurable business value?
Building systems that scale globally while delivering real business value requires a careful balance of technical excellence and practical business outcomes. Here are few of the several core principles I believe are necessary. First and foremost is the concept of measured deployment, where changes are rolled out through carefully monitored phases. This isn’t just about technical caution but helps create a feedback loop that allows organizations to measure business impact at each stage. For instance, in customer-facing features such as performance Dashboard, this might mean starting with a 25% user base and gradually expanding while optimizing before full deployment.
Data-driven decision making forms another cornerstone principle. Every technical decision needs clear business metrics behind it. Take a returns management system – its success isn’t measured just by technical performance but by concrete outcomes like reducing processing costs by 18% and improving resolution times by 25%,, translating to millions in cost savings. The third principle focuses on global adaptability – building systems that work effectively across different markets while maintaining consistent performance. This means handling multiple languages, adapting to regional business rules, and complying with local data requirements without compromising system efficiency.
Risk management and operational excellence form the fourth pillar. This involves comprehensive monitoring systems, clear escalation paths, and robust contingency plans. It’s about ensuring that as systems scale globally, they maintain reliability while giving businesses the agility to respond to market changes quickly.
Finally, successful global systems require strong stakeholder alignment.
This means creating clear communication channels between technical teams, business units, and end users, ensuring that technical capabilities align with business needs and user expectations. When implemented together, these principles create systems that not only scale technically but deliver measurable business value across global markets. They provide the foundation for sustainable growth while maintaining operational excellence.
Your research spans areas like AI in personalized medicine and ethical AI decision-making. How do you balance cutting-edge innovation with responsible and explainable AI deployment in commercial settings?
This is a responsibility I take very seriously. Innovation is only meaningful if it can be trusted—especially when decisions affect people’s health or livelihoods. I remember one case in the personalized medicine project where a doctor asked, “Why did the model suggest this treatment?” That moment really stayed with me. We had built interpretability into the model using SHAP, and seeing that trust build in a real clinical conversation reinforced the importance of transparency. Even in commercial settings like e-commerce, we apply fairness-aware modeling and validation checks because I believe that every advanced system should also be an accountable one.
From launching multilingual portals to re-architecting latency-sensitive systems, your work shows technical depth. Can you share one of your toughest engineering challenges and how you tackled it?
Transformed a manual law enforcement case management system from a monolithic shared-mailbox architecture into an automated, event-driven solution using microservices. The new system efficiently processes thousands of monthly cases through a streamlined external portal accessible to law enforcement officials. Implemented a serverless workflow using AWS Step Functions and Lambda to handle case creation and assignment to Legal Specialists, significantly reducing debugging efforts and improving overall system reliability. Designed the architecture to support global scalability, enabling seamless expansion across multiple continents including Europe, Asia, and North America through strategic use of CloudFront distribution and regional API endpoints. Enhanced user experience with language localization support and an automated notification system for effective communication with law enforcement officials. Since launch, the system has demonstrated substantial improvements in security, efficiency, and scalability compared to the original monolithic approach, marking a pivotal shift in how law enforcement requests are managed across diverse international jurisdictions.
You’ve served as a reviewer for journals and conferences. How has that shaped your own research and development work?
Reviewing other researchers’ work has made me a more thoughtful and precise innovator. I’ve had moments reading papers where I thought, “This is brilliant—but where’s the proof?” That made me more self-aware about my own assumptions. It’s one thing to build something—another to explain it, validate it, and defend its value. Through peer reviewing, I’ve learned to ask deeper questions, refine my thinking, and write more clearly. It’s even changed how I mentor juniors—I now encourage them to think like reviewers, not just coders.
You recently received the IARC Technology Leader of the Year award. What did this recognition mean to you?
It was an honor I deeply value. The International Achievements Research Center recognized my contributions to software development at scale, my research in AI, and the mentorship I’ve provided through talks and judging programs. What made the award meaningful was that it reflected not just one project, but the entire journey. I thought back to the late nights debugging critical issues before a launch, or the moments when a student I mentored landed their first research paper. It reminded me that building systems is only part of the story—building people matters just as much.
You’ve spoken at colleges about AI in e-commerce and mentored students on career paths. What advice do you give to engineers starting out today?
The tech landscape is fast, and it’s easy to feel overwhelmed—but the best engineers I know are those who stay curious and grounded. I always tell students to build real-world projects, ask good questions, and not fear failure. I remember building one of my early prototypes—it completely failed, but I learned more from that experience than from three courses combined. Tools will evolve, but critical thinking and a willingness to learn will always be in demand. I also encourage them to give back—mentoring others teaches you more than you might expect, and the tech community thrives when we lift each other up.
Your work on optimizing transformer models for low-latency inference is timely. What trends do you see in deploying AI in real-time systems?
There’s a huge demand for models that are both powerful and fast. In one of my projects, we optimized transformer models to serve recommendations in under 50ms by combining quantization, precomputed embeddings, and model distillation. I still remember the excitement of seeing latency drop below our threshold in production—it felt like magic. Trends I’m seeing include edge deployment, dynamic model routing based on latency constraints, and hybrid inference strategies that mix local caching with lightweight online prediction. AI isn’t just about accuracy anymore—it’s about being fast, efficient, and scalable in the real world.
You’ve contributed to both academic and industry circles—as a judge, reviewer, and speaker. How important is that cross-sector collaboration for innovation?
It’s critical. I’ve found that real innovation happens when you connect the theoretical depth of academia with the execution power of industry. One of my favorite experiences was mentoring a student team whose paper idea turned into an actual product prototype within a few months—it was inspiring to see that bridge work in real-time. My experience at Amazon helps me guide academic ideas toward impact, and my academic work keeps me questioning and curious. Bridging these worlds is one of the most rewarding parts of my journey.
Looking ahead, what emerging areas excite you, and where do you plan to focus next?
I’m really excited by the intersection of real-time AI, personalization, and human-centered systems—especially in areas like accessibility, sustainability, and digital health. I’ve been fortunate to work on problems that touch real people’s lives, and I want to keep doing that. I also care deeply about mentorship and diversity in tech—so I plan to keep speaking, judging, and mentoring as part of that mission. Ultimately, I want to use AI not just to optimize systems, but to uplift people.
From the editor…
From scaling transformer models for low-latency inference to teaching students how to make the leap from academia to industry, Apoorva Kasoju has approached her career with a sense of wonder, rigor, and a commitment to using technology to improve lives. As AI systems become more powerful, voices like her are a reminder that our work should be judged not just on performance metrics, but on the human impact our systems have.
