MEMBER PROFILE
Daigo Hiroka
ML Engineer・MLOps
Background
After working as a machine learning engineer at a data analytics consulting company, I joined CADDi in December 2022 as part of the AI Lab MLOps Team, where I was involved in the development and operation of a machine-learning-based drawing analysis system. From 2023, I also worked as an SRE for CADDi Drawer, handling infrastructure maintenance and the promotion of SLI/SLO operations. In 2024, I moved to the Analysis Platform Group, where I once again focuses on MLOps as well as building inference infrastructure and ensuring reliability for machine learning systems.
Why I joined CADDi
As I considered changing jobs, I noticed that CADDi had started investing seriously in machine-learning-based drawing analysis and seemed to be attracting strong talent as well. One of my key criteria was that I wanted to be involved in building and operating a service that would continue growing globally. I could see that engineers and business-side members at CADDi were working together as one to drive the growth of the business. I felt strong potential in what the company could become and decided that I wanted to dive into that growth myself. Since joining as an engineer on CADDi Drawer and working on areas such as SRE and machine learning infrastructure, I have once again come to appreciate both the difficulty and the excitement of building a scalable service for a growing user base and expanding amounts of data. It is not an easy challenge, but it feels absolutely worth betting my engineering career on.
My work at CADDi
At CADDi Drawer, I contribute to engineering mainly from two angles: SRE and MLOps. As an SRE, I have worked to improve the service's reliability through infrastructure maintenance, the promotion of SLI/SLO operations, incident response, and emergency preparedness such as on-call systems. As an MLOps engineer, I work on the machine learning backend for drawing analysis and use my SRE experience to improve SLI/SLO and other non-functional requirements, all with the goal of building a scalable and highly reliable analysis platform. Looking ahead, we expect further growth in data volume, data types beyond drawings, and the range of analyses we want to support, so I intend to continue building out a scalable machine learning platform that can keep up with that expansion.
Go-to-Market
Engineering
Corporate