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Strong employer brand, metro location, and mid-level seniority increase applicant competition.
ML engineering and MLOps skills are widely transferable across industries, reducing background sensitivity.
Explicit years, mandatory MLOps/AWS/ML frameworks, and leadership expectations enforce strict filtering.
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Develop and implement scalable machine learning and generative AI solutions impacting Nike’s corporate functions business decisions.
Design and build advanced analytics, prediction, and optimization platforms and tools to enable model development and deployment.
Collaborate with global teams and stakeholders to deliver robust AI capabilities integrating with upstream and downstream technology systems.
Bachelor's degree in Computer Science or Master's in related engineering field or equivalent experience.
Minimum 5 years professional software engineering experience, with at least 3 years in Machine Learning Engineering or related fields.
Proficiency in Python, AWS (especially ECR, SageMaker, Lambda, API Gateway), containerization (Docker), and CI/CD automation.
Experience with ML frameworks (Scikit-learn, PyTorch, Tensorflow), data engineering (SQL, ETL, Spark), and pipeline orchestration tools (AirFlow or Databricks).
Experienced in leading technical strategy and providing mentorship within agile engineering teams focused on ML/AI solutions.
Strong expertise in MLOps and the machine learning lifecycle from experimentation through production and measurement.
Comfortable working in a globally distributed team environment delivering enterprise-scale AI applications for business impact.