As AI grows in scope and utility, the industry is at a crossroads, with centralized, cloud-based systems beginning to show their cracks.
Latency, energy demands, and privacy risks are just the beginning. The real question isn't whether AI can think, but if it can think fast, safely, and reliably enough for the real world.
These challenges have led engineers to explore edge-native AI, which shifts intelligence out of centralized clouds and closer to users: into their phones, vehicles, homes, and even environments where lives are on the line. For this to succeed, systems have to recognize context, make smart use of available resources, and stay operational even when parts of the system fail.
Software engineer Abhigyan Khaund has worked on these kinds of systems at companies like Microsoft, Meta, and Palantir. From cold-start recommendation engines and fraud detection systems to enterprise-grade infrastructure and AI protocol design, Khaund has experience across the layers where AI meets the real world.
For him, the shift is about more than performance, it's about trust, context, and reliability: "The end goal is to make AI tools feel less like magic in the cloud, and more like something reliable and useful in your pocket."
Here's a closer look at how he's making it happen.
Lessons from the Past: Where Intelligence Fails without Infrastructure
Khaund has spent years building and refining intelligent systems on a global scale.
At Microsoft, he was responsible for handling how user data was synchronized across different organizations. This problem brought up real challenges in latency, consistency, and scaling APIs to handle millions of users. An important issue he worked on was reducing the time it took for a new user to be onboarded into a shared workspace.
By optimizing how policies were evaluated during that process, he helped bring down the API response time by 10x. The fix required deep architectural debugging, cross-team coordination, and system-wide load balancing, but the real takeaway was more than technical. "Overcoming challenges isn't just about technical expertise," he explains. "It's also about building relationships and trust, and staying focused on the bigger picture to push through adversity."
At Meta, he worked on applying reinforcement learning for fraud detection, where model quality was just one piece of the puzzle. The real bottlenecks came from slow inference paths in production, fragile integrations across microservices, and difficulty scaling decision pipelines without degrading performance.
Before these roles, Khaund co-developed IceBreaker, a cold-start video recommendation engine that used deep learning ensembles to deliver relevant content without relying on user interaction data.
"Solving hard problems isn't about having all the answers up front," he says. "It's about being okay with uncertainty, breaking things down, and pushing through when it gets frustrating."
Resilience in Distributed Systems with RD-FCA
Khaund has always gravitated toward systems that can handle real-world problems like failure and unpredictability, which led him to prioritize resilience and scale for complex data workflows.
He helped build RD-FCA, the first resilient distributed framework for formal concept analysis (FCA), which he co-developed and co-authored during his undergraduate research. FCA is a powerful data mining technique, but it's traditionally difficult to scale due to its computational complexity and irregular recursion patterns. The problem gets even tougher in distributed environments, where nodes can fail mid-execution, potentially requiring the whole computation to restart.
That's where RD-FCA came in. Khaund's role on the project was focused on designing and implementing fault-tolerance mechanisms, dynamic load-balancing strategies, and a recovery architecture that could handle various types of failures, whether single, multiple, or cascading.
RD-FCA made a resounding impact, and it quickly became the first known framework for FCA that could efficiently recover from failures without losing progress. It scaled well on both dense and sparse datasets and helped advance the applicability of FCA to real-world, large-scale problems such as community detection, recommendation systems, and document summarization.
Seeing this work published in the Journal of Parallel and Distributed Computing was incredibly rewarding for Khaund, not only for the recognition but also because it addressed a significant bottleneck in large-scale knowledge discovery and data analysis.
MCP and the Modular AI Stack: A New Protocol for Distributed Intelligence
To support the next generation of AI agents, especially ones that live and act on personal devices, a new kind of infrastructure language is required. For Khaund, that language is the model context protocol (MCP), which he sees as a foundational shift.
MCP is to AI what APIs were to web services: a modular interface that allows different agents, models, and environments to communicate seamlessly while preserving security and state.
In his view, this is how AI becomes not only more responsive but also more personal: "Whether that includes optimizing models to run on constrained hardware, or building the backend that supports low-latency, secure decision-making across devices, I want to help push that forward."
The Road Ahead: Building Trustworthy AI That Lives Closer to the User
Khaund's long-term goal is to shape infrastructure that makes personal-device AI both technically sound and ethically responsible. For him, it's not enough for AI to function; it must function with intention.
That means designing systems where tradeoffs are clear and deliberate, such as choosing between performance and battery life based on user needs. It also means building fallback mechanisms that preserve trust when things don't go as planned.
Most importantly, Khaund believes that trust comes not only from transparency but from consistency in how systems behave, especially under pressure.
For AI engineers, systems designers, and infrastructure architects, his work is a clear call to prioritize the invisible layers of engineering that make intelligence safe, responsive, and real-world ready. On-Device AI will need smarter systems shaped by people who understand what's at stake.
"I've learned that elegant systems aren't the ones with the fanciest architecture," he concludes, echoing a lesson from a mentor at Microsoft. "They're the ones that stay standing when things go sideways. You earn that by obsessing over the edge cases most people don't see."
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