Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 11


Data Mesh / Fabric - design and implementation, provides flexible, resilient integration of data sources across platforms and business users, making data available everywhere it’s needed regardless of where the data lives (AWS, Azure)
	Cloud-native platforms – developing business-focused applications that leverage advanced  technologies. It allows businesses to build new application architectures that are resilient, elastic, and agile — enabling companies to respond to rapid digital change
	Composable applications - next step in microservices approach, applications are built from business-centric modular components (Python, Golang, Javascript, Typescript)
	Decision intelligence - providing companies with methodology and roadmaps to improve organizational decision making. It models each decision as a set of processes, using intelligence and analytics to inform, learn from and refine decisions
	AI Engineering – leading projects to automate updates to data, models, and applications to streamline AI delivery.
	Generative AI  - investigating a variety of opportunities (“known unknowns”) to learn about artifacts from data, and generates innovative new creations that are similar to the original but don’t repeat it. Generative AI has the great potential to create new forms of creative content, such as video, and accelerate R&D cycles in a variety of business fields.
	NFT - use cases (gaming, music industry), NFT Smart Contracts, NFT Minting, IPFS storage, and NFT Security (ERC-721 Smart Contracts)