Mirriad Core Technologies

At Mirriad I had the opportunity to work on the core R&D team working on the high-level architecture deployment of the company proprietary Machine Learning models for the automation process of Native In-video Placement.

I helped to raise at the enterprise level, the maintenance process of the main desktop tools used in post-production (Linux/Windows Desktop) ensuring:

• A high-quality dependency management
• High-performance compilation and deployment system across multiple geographic locations.
• Dedicated Qt UI galleries for the dynamic loading of heterogeneous content stored across different cloud providers (country-specific legal requirements/constraints)

I designed from scratch, implemented and deployed a new quality process verification for KPI definition of the quality of the video inventory at company disposal. This is to improve the general preemptive profit assessment of new video ingested in our inventory from the new bigger content provider (scaling toward Alibaba/Youku video content).

After that, I was promoted as Principal SW Engineer and Team Manager and I span off a new team responsible for the productisation level of the Creative and R&D tools. 

Now I am responsible for the following activities:

• Migrated a 1.5 mln code made to a CMake/Conan based building system with non-incremental build time reduced of 80%.
• Leading the product-level development of features of company IP core AI technology stack and all their DevOps infrastructure.
• Responsible for the Creative Video Editing Desktop Tools applications of the company.
• Led the design of the new generation API for the core team.
  Deployed to Tencent Cloud in Shanghai Data Center the full video stack production that serves 600 mln streaming customers every day.
• Transforming the internal academic computer vision and AI inference to infrastructure as a code easily deployable to customer cloud premises.