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Tier-1 brand, mid-level generalist ML role, metro location, and broad skillset drive high applicant competition.
ML engineering skills are broadly transferable across industries, so background sensitivity is medium.
Explicit MSc/PhD requirement, 5+ years experience, and mandatory ML/MLOps and coding skills.
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Design, build, and maintain scalable, production-grade machine learning systems and pipelines applying novel algorithms beyond experimental validation.
Collaborate in cross-disciplinary teams to translate complex scientific and business problems into deployable ML products delivering measurable value across NLP, optimisation, simulation, generative AI, and scientific/R&D domains.
Mentor junior members, present technical results to stakeholders, and contribute to engineering standards, developer velocity, and operational excellence with MLOps and CI/CD practices.
MSc or PhD in Computer Science, Mathematics, Physics, Engineering, or related quantitative field.
Minimum 5+ years experience designing, productionising, and scaling ML/data science products in complex environments.
Strong programming skills in Python, Go, Java, or C++, with advanced SQL knowledge and experience in modern ML engineering practices including MLOps and CI/CD.
Up to 10% travel; role supports hybrid work; relocation within country possible.
Experienced ML engineer with strong balance of deep machine learning science and software engineering to deliver production-grade ML products.
Able to navigate end-to-end ML product lifecycle from novel algorithm development to deployment and operational use in complex environments.
Capable of influencing cross-functional teams including data scientists, engineers, and domain experts, and mentoring junior staff, in a fast-evolving technology organization.