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Tier-1 brand and hybrid remote increase interest, but senior, niche ML requirements restrict applicant density.
Role demands deep applied ML science and platform leadership, limiting easy transferability across unrelated industries.
Explicit 8+ years, advanced degree, and deep ML research-to-production mandates create strict shortlisting filters.
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 research and algorithm design through to deployment and product delivery.
Define technical standards and best practices for ML engineering and applied ML science, mentoring senior engineers and driving multi-team initiatives with measurable organisational impact.
MSc or PhD in a quantitative discipline (e.g., Computer Science, Mathematics, Physics, Engineering).
Typically 8+ years of hands-on experience designing, prototyping, productionising, and scaling complex ML systems in production environments.
Advanced programming skills in Python, Go, Java, or C++; strong software engineering and system design expertise including distributed systems and scalable architectures.
Strong experience with MLOps, production ML systems, model lifecycle management, monitoring, and large-scale data systems (e.g., Spark, Hadoop).
Deep expertise in applied machine learning science with a proven track record of developing and deploying novel algorithms and scientific models as reliable, scalable products.
Experience leading technical excellence and delivering high-impact, cross-organisational ML initiatives bridging scientific research and enterprise deployment.
Capability to influence large organisations without direct authority and mentor senior engineers and data scientists.