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Metro location and strong global brand, but senior specialized role limits applicant density.
Advanced applied ML science, PhD preference, and scientific computing focus limit transferability across industries.
Explicit 8+ years, MSc/PhD, advanced ML, MLOps, and publication/patent expectations make filters highly strict.
Lead design and architecture of large-scale, production-grade machine learning systems and platforms across the organisation.
Own end-to-end delivery of complex ML solutions from scientific problem framing, algorithm design to deployment and operationalisation.
Drive applied ML science innovation by developing novel algorithms and ensuring their scalable, reliable production deployment while mentoring senior engineers.
Master's or PhD degree in a quantitative field such as Computer Science, Mathematics, Physics, or Engineering.
8+ years of hands-on experience designing, prototyping, productionising, and scaling complex ML systems in production.
Strong software engineering skills including distributed systems, scalable architectures, API design, advanced programming (Python, Go, Java, C++), and advanced SQL knowledge.
Experience with MLOps, model lifecycle management, monitoring, large-scale data systems (Spark, Hadoop), and scientific methodology.
Experienced technical leader capable of defining ML system architecture and technical standards across multiple teams and business domains.
Strong background in applied machine learning science with practical experience translating research into deployed enterprise-grade ML products.
Proven track record in innovation (publications, patents, open source) and integrating emerging AI approaches including generative AI, Agentic AI, and scientific computing in production environments.