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High competition due to Tier-1 brand, metro location, broad skillset, and global-scope role.
Medium because enterprise-scale data and MLOps skills transfer across industries but favor cloud/AI contexts.
High due to explicit 12+ years, 7+ years managerial, and mandatory cloud/MLOps technical skills.
Lead design and architecture of scalable enterprise data pipelines, lakehouse infrastructure, and end-to-end ML model development ensuring high availability and fault tolerance.
Oversee deployment, serving, scaling, and monitoring of machine learning models in production with robust MLOps practices.
Manage and mentor a team of data and ML engineers, driving engineering rigor, autonomy, and alignment with business objectives.
12+ years total experience in data engineering and MLOps.
7+ years managing data and/or ML engineering teams.
Hands-on expertise with modern data platforms (especially Google Cloud Platform), distributed computing, Python, and SQL.
Role is not eligible for immigration sponsorship (work visa) per the JD.
Experienced player-coach capable of hands-on technical guidance including code reviews and ML architecture discussions.
Proven ability to deliver enterprise-scale, high-throughput data and AI solutions across global stakeholders.
Strong operational focus on CI/CD, data governance, quality metrics, and scalable MLOps frameworks in an enterprise environment.