As an engineer, I was drawn to the intricacies of high-precision robotics. During my undergraduate years, I dove headfirst into embedded systems, working hands-on with CPLDs, FPGAs, and microcontrollers. For many, these technologies might seem arcane, but for me, they represented an alluring challenge—manipulating robot arms, toy cars, and processing computer vision data in ways that brought theoretical concepts to life. These machines, so complex yet precise, demanded not just technical know-how but the capacity to make them work flawlessly in a messy, real-world environment.
This fascination guided me to Applied Materials, a global leader in semiconductor manufacturing equipment. At Applied, I found myself working with fabrication atmospheric and vacuum robots, measuring their reliability and ensuring their precision. These robots handled silicon wafers with sub-millimeter accuracy at high speeds, moving them from one chamber to another in an exacting dance of technology. It wasn’t just a job—it was an introduction to the nuanced, high-stakes world of robotics in semiconductor fabrication, a world where even the smallest mistake could have enormous repercussions. But for me, it was also more than that. As a newly arrived immigrant to the U.S., the job was my first real foray into American corporate culture and the high-tech sector. It was a proving ground.
My curiosity only deepened at Stanford, where I plunged deeper into micro and nanosystems, designing MEMS, nanostructures, and chip architectures. Cryptography, always a tantalizing area of interest from my undergraduate time studying information theory, led me to coursework in the domain. I took Dan Boneh's course, drawn not just to the theoretical elegance of cryptography, but to how these concepts were applied in real systems demanding security and scalability. These academic pursuits intertwined with the practical systems I had encountered regularly and a desire to deeply explore different infrastructures led me to better understand its inner workings and broader applications.
Then, an opportunity presented itself. Hewlett Packard Enterprise (HPE), a company with a rich legacy in data security, needed an engineer who could grasp the physics of thermodynamics as well as the intricacies of electronic systems—both critical to solving a growing problem in their next-generation Hardware Security Modules (HSMs).
At HPE, the work was as exacting as it was exhilarating. We were tasked with developing HSMs capable of performing cryptographic operations at scale, including managing the keys for e-commerce and securing sensitive data. This was no simple job—our hardware was a 23-layer PCIe card, complete with a Faraday cage for electromagnetic protection and a custom thermal solution to handle the heat generated by high-density cryptographic chips. It was a massive technical challenge, compounded by the fact that our team was small—just three engineers working intensely for 20 months to design, prototype, and ship the product. When it finally went live, it was more than just a success. It was proof of what a small, tight-knit team could accomplish with a multidisciplinary approach.
As the product development at HPE wrapped up, my interest shifted toward Bitcoin and the burgeoning world of blockchain, payments, and identity. Cryptography and hardware security were already familiar terrain, but blockchain represented something new—a frontier that was just beginning to be explored. An old manager from HPE reached out around this time with a proposition: to join a startup called Neutrno, aimed at building a platform for securing IoT devices. Code signing, over-the-air updates, and a distributed software registry all came into play, but ultimately, the product was ahead of its time. While the market struggled to catch up, the experience at Neutrno opened my eyes to the challenges of building early-stage technology companies. It set the stage for my next venture.
Bitski marked a return to hardware security, but this time in the context of blockchain. We were building secure wallet infrastructure for a rapidly expanding ecosystem, and the lessons learned at HPE became invaluable. The blockchain space was fast-moving, and few in it understood HSMs well. This, combined with my dive into modern DevOps practices like GitOps and Terraform, gave me the tools to navigate the intersection of blockchain, decentralized tech, and operational infrastructure. It was also during this time that I received my acceptance letter to Stanford Business School—a new chapter awaited.
Syndicate Protocol came next, where I led the Go-to-Market and Sales team, helping build a platform that married decentralized technology with SEC compliance, making cap-table management and investment circles a reality in both crypto and traditional assets. It was a high-stakes, highly regulated environment, and I thrived on the challenge of balancing innovation with regulation. But as crypto faced increased fraud and regulatory scrutiny, I found myself at a crossroads.
That’s when Aineko was born—a venture built with a few Stanford colleagues to solve the issue of fraud detection. What began as a tool to secure blockchain transactions quickly evolved into something much larger: a real-time inference system, capable of streaming data pipelines and deploying AI-powered applications. Aineko became a multi-faceted platform: from open-source Python tools for real-time ETL, to serverless cloud deployments, to Knowledge Graph Retrieval-Augmented Generation (RAG) systems enhancing LLM performance.
The journey, in hindsight, wasn’t linear, but it was bound by a constant thread: a relentless curiosity for how things work and how they can be improved. Whether it was the robots at Applied Materials or the AI frameworks at Aineko, the underlying principle was the same—an insatiable drive to understand complex systems and make them better. Every step, every pivot, was part of a larger story. A story of curiosity, problem-solving, and, above all, a belief that with enough determination, even the most intricate challenges can be solved.