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Tier-1 brand and metro location but senior, specialized role reduces applicant density.
Requires advanced applied ML science and platform experience, limiting cross-industry transferability.
Explicit 8+ years, MSc/PhD requirement and deep ML science/multi-team leadership filters are strict.
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Lead design and architecture of large-scale, production-grade machine learning systems and platforms organization-wide.
Own end-to-end delivery of complex ML solutions from scientific research through deployment and operationalization.
Develop, validate, and deploy novel ML algorithms and scientific models as scalable, reliable production products, while mentoring senior engineers and influencing multi-team initiatives.
MSc or PhD in a quantitative field such as Computer Science, Mathematics, Physics, or Engineering.
Typically 8+ years of hands-on experience designing, prototyping, and scaling complex ML systems in production environments.
Strong software engineering skills in distributed systems, scalable architectures, APIs, and programming languages like Python, Go, Java, or C++.
Advanced knowledge of MLOps practices, production ML lifecycle management, SQL, large-scale data systems (e.g. Spark, Hadoop), and experimental scientific methodologies.
Demonstrated expertise in applied machine learning science with ability to translate novel algorithms and scientific research into production-grade ML products.
Experience with advanced AI techniques including generative AI (LLMs, RAG), Agentic AI systems, and scientific/R&D workflows such as simulation, optimisation, or autonomous scientific processes.
Skilled in driving technical strategy and standards across multiple teams with proven leadership delivering high-impact ML systems in complex organizational environments.