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Tier-1 brand, popular ML role, mid-level experience, metro location, and broad skill requirements increase applicant competition.
Strong ML engineering skills are transferable, though scientific/R&D emphasis adds moderate domain bias.
Explicit 5+ years requirement, MSc/PhD essential, and mandatory production MLOps skills.
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Design, build, and maintain scalable, production-grade machine learning systems and pipelines with strong engineering practices (CI/CD, monitoring).
Develop and deploy novel machine learning algorithms and models that transition from research to reliable, scalable production products, including NLP, optimisation, simulation, and generative AI.
Collaborate with cross-disciplinary teams to translate complex scientific and business problems into actionable ML solutions and mentor junior members.
MSc or PhD in Computer Science, Mathematics, Physics, Engineering, or related quantitative field.
5+ years of hands-on experience designing, prototyping, productionising, and scaling ML/data science products in complex environments.
Strong expertise in machine learning algorithms, statistical modelling, optimisation techniques, and advanced programming skills (e.g., Python, Go, Java, C++).
Experience with modern ML engineering practices including MLOps, model lifecycle management, CI/CD, and monitoring.
Deep expertise in applying machine learning science for production deployment beyond experimentation, with a track record of delivering measurable value.
Experience working in cross-disciplinary teams combining data science, software engineering, and domain expertise to build impactful ML products.
Comfortable handling complex scientific and business problems, mentoring peers, and influencing stakeholders at various levels.