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Tier-1 brand and metro location increase competition, but senior ML specialization reduces applicant density.
ML and MLOps skills are transferable, but principal-level enterprise experience increases background sensitivity.
Explicit 10+ years, mandatory ML experience and specific tech stack requirements make filters highly strict.
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Own end-to-end delivery of AI/ML and generative AI projects impacting corporate functions, including design, development, and operationalization of scalable machine learning solutions.
Provide technical vision, leadership, and mentorship within the AI/ML engineering team to influence strategy and ensure robust, maintainable code.
Collaborate with global teams and business stakeholders to build prediction models, optimization programs, and advanced analytics platforms for data-driven decision making.
Bachelor's degree in Computer Science or Master's in a related engineering field or equivalent experience.
10+ years professional software engineering experience; 3+ years specifically in Machine Learning Engineering or related fields.
Proficiency in Python, data structures, AWS cloud technologies, databases (Postgres, Redis), and data processing tools (Sagemaker, Databricks).
Experience with containerization (Docker), CI/CD, agile development, test-driven development, and end-to-end project ownership.
Strong technical leadership with proven ability to mentor and manage team members on complex AI/ML projects in an agile, collaborative environment.
Experience working with global, distributed teams building scalable machine learning and generative AI applications with cloud architectures (especially AWS).
Demonstrated expertise in machine learning lifecycle including MLOps, API development, ETL pipelines, and deploying predictive or optimization models to production.