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Strong brand and a mid-level ML role raise competition, though ML specialization moderates applicant density.
Specialized ML, MLOps, and cloud requirements increase domain-specificity, reducing cross-industry transferability.
Multiple explicit filters—5+ years, 3+ ML, MLOps, AWS, frameworks and leadership—make shortlisting highly strict.
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Design and implement scalable AI/ML and generative AI applications driving data-driven business decisions for corporate functions.
Contribute to advanced analytics, machine learning platforms, and tools for prediction and optimization model development.
Collaborate closely with global teams, business stakeholders, and technology groups to deliver and coordinate AI/ML solutions at scale.
Undergraduate degree in Computer Science, Master’s in related engineering field, or equivalent experience.
5+ years professional software engineering experience, including 3+ years in machine learning engineering or related fields.
Proficiency in Python programming, AWS services (ECR, SageMaker, Lambda, API Gateway), and MLOps lifecycle.
Experience with containerization (Docker), CI/CD automation, frameworks like Scikit-learn, PyTorch, Tensorflow, and data technologies (Postgres, Redis, ETL, Spark).
Experienced technical leader comfortable providing mentorship and influencing AI/ML technical strategy across teams.
Strong background in designing robust, maintainable, and scalable machine learning systems within an agile product development environment.
Familiar with complex data architectures, cloud technologies, and orchestration tools (AirFlow, Databricks Workflows), thriving in a collaborative, knowledge-sharing global engineering team.