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Tier-1 employer, popular mid-level ML role with broad MLOps and research requirements increases applicant competition.
Core ML engineering skills transfer across industries, but research and scientific-R&D emphasis raises domain specificity.
Requires MSc/PhD, explicit 5+ years, production MLOps experience and strong programming skills.
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Design, build, and maintain scalable, production-grade machine learning systems with a focus on operational deployment and reliability.
Develop novel ML algorithms and models and transition them seamlessly from research to production to deliver measurable business value.
Collaborate cross-functionally with data scientists, engineers, and domain experts to translate complex problems into deployable ML products and mentor junior team members.
MSc or PhD in Computer Science, Mathematics, Physics, Engineering, or related quantitative field.
Typically 5+ years of hands-on experience building and scaling production-grade machine learning/data science products.
Strong programming skills in at least one object-oriented language (e.g., Python, Go, Java, C++) and advanced SQL knowledge.
Experience with ML engineering practices such as MLOps, model lifecycle management, CI/CD, and monitoring.
Experienced in applying advanced machine learning, statistical modelling, and optimisation techniques to build reliable, scalable ML systems.
Thrives in cross-disciplinary teams to translate scientific and business challenges into practical ML solutions with a focus on value delivery.
Familiarity or experience with big data technologies, generative AI, and scientific or R&D workflows deploying AI/ML products is a plus.