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Tier-1 employer, popular ML role, metro location, and broad skill requirements increase competition.
Core ML engineering skills are transferable, but emphasis on scientific computing and R&D raises domain specificity.
Explicit 8+ years, MSc/PhD requirement, and deep ML/MLOps expertise create strict filters.
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Lead design and architecture of large-scale, production-grade machine learning systems across the organisation.
Own end-to-end delivery of complex ML solutions from problem framing and algorithm design through deployment and operationalisation.
Develop and deploy novel ML algorithms bridging research to scalable, reliable production products, while influencing multiple teams and setting engineering standards.
MSc or PhD in quantitative field such as Computer Science, Mathematics, Physics, or Engineering.
Typically 8+ years hands-on experience designing, prototyping, productionising, and scaling complex ML systems in production.
Advanced programming skills in Python, Go, Java, or C++; strong SQL and experience with large-scale data systems and distributed computing frameworks (e.g., Spark, Hadoop).
Deep expertise in ML algorithms, statistical modelling, optimisation, MLOps, model lifecycle management, and monitoring.
Proven technical leader able to architect and deliver organisation-wide, high-impact ML initiatives with multi-team influence.
Strong background in applied ML science, translating research innovations into maintainable, production-grade products.
Experience with generative AI, Agentic AI systems, and building ML/scientific computing platforms is highly preferred.