Bachelor’s degree in Computer Science, Information Systems Management or in similar fields, Master/PhD is a bonus.
5+ years professional experience as a Software Engineer, SysOps Engineer, Software Engineer, API Engineer, or similar role with proven experience in software development and operation roles in a cloud environment.
Proven experience in following technologies is a must: microservices architecture, AWS and public cloud, REST APIs design, Pub/sub and event-drive architecture, CICD, infrastructure-as-code, AI in SDLC.
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Drives for results and achieves technical objectives within timelines e.g. customer approvals, commercialization of products, realization of technical value propositions
Develops and nurtures technical relation-ships with customers counterparts , to enhance understanding of user technical feedbacks and adhesion to LYB product technical advantages.
Approvals of products at customers with growth opportunities
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End-to-end technical ownership of the Go microservices architecture: service design, inter-service communication patterns, and long-term technical direction
First technical review point for new product and feature requests — shaping service boundaries and integration patterns before implementation begins
Definition and evolution of backend engineering standards: API design conventions, Go idioms, code quality practices, testing approaches, and shared patterns
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End-to-end technical ownership of the Go microservices architecture: service design, inter-service communication patterns, and long-term technical direction
First technical review point for new product and feature requests — shaping service boundaries and integration patterns before implementation begins
Definition and evolution of backend engineering standards: API design conventions, Go idioms, code quality practices, testing approaches, and shared patterns
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Deliver highly reliable Computer Vision features by writing clean, modular, and optimized pipelines that achieve target accuracy metrics, minimize false positives/negatives, and meet the "Definition of Done."
Drive the squad’s technical growth through structured reviews of both software code and model architectures, ensuring all engineers are proficient in the "Universal Stack" and modern Computer Vision deployment frameworks.
Design modular Computer Vision processing pipelines that easily integrate across squads via robust API contracts, making it simple for the TRE team to monitor frame-ingestion, queuing, and model outputs.
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Support the planning and execution of AI and semiconductor initiatives, including workshops, business matching, ecosystem engagements, and industry programmes under AI Connect
Assist in coordinating with innovators, technology companies, corporates, universities, research institutions, investors, and ecosystem partners.
Support ecosystem-building activities such as industry dialogues, workshops, showcases, networking sessions, and technology engagement programmes.
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