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A leading educational institution in Singapore is seeking a Platforms Engineer (MLOps) to develop and maintain essential infrastructure for Machine Learning workflows. The ideal candidate will have a background in engineering, proficiency in programming languages like Python and Typescript, and experience with MLOps and container technologies. This role involves mentorship and collaboration in a dynamic team environment focused on advancing Singapore's AI capabilities.
Interested applicants are invited to apply directly at the NUS Career Portal. Your application will be processed only if you apply via the NUS Career Portal. We regret that only shortlisted candidates will be notified.
AI Singapore (AISG) is a national AI programme launched by the National Research Foundation (NRF) to anchor deep national capabilities in Artificial Intelligence (AI).
The programme office is hosted by the National University of Singapore (NUS) and brings together all Singapore-based research institutions and the vibrant ecosystem of AI start‑ups and companies developing AI products to perform use‑inspired research, grow the knowledge, create the tools, and develop the talent to power Singapore's AI efforts.
Since our inception in 2017, we have established a culture of respect, continuous learning, experimentation and curiosity, with all our work centered around innovation.
The Platforms Engineering team is looking for a Platforms Engineer (MLOps) to join us on our journey to build and operate modern on‑premise and cloud infrastructure and systems to enable and accelerate Machine Learning workflows for our internal teams.
In this role, you will focus on building and maintaining our internal MLOps platform across our on‑premise and cloud infrastructure. You will also serve as a domain expert to nurture AI talents and will play a key part in driving the growth of Singapore’s AI Sector.
* Il benchmark retributivo si basa sugli obiettivi retributivi dei leader del mercato nei rispettivi settori. È pensato per orientare gli utenti Premium nella valutazione delle posizioni aperte e aiutarli a negoziare la propria retribuzione. Tale benchmark non è fornito direttamente dall'azienda, quindi la retribuzione effettiva potrà risultare anche notevolmente superiore o inferiore.