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 Role Descriptions: An Applied ML Engineer to design| deploy| and operationalize machine-learning models that power the NBA Decision Engine| enabling intelligent selection of the most relevant Next Best Action using real-time and historical data. This role focuses on building and integrating Python-based ML models (e.g.| propensity| ranking| uplift| or optimization models)| serving models via low-latency APIs| and integrating with feature stores| streaming pipelines (Kafka or equivalent)| and decisioning services built in Node.js andor Python. The ideal candidate has strong experience with ML frameworks (e.g.| TensorFlow| PyTorch| scikit-learn)| MLOps practices (model versioning| monitoring| retraining)| and understands explainability| bias| and guardrails in regulated environments. The engineer will collaborate closely with data engineers| rules engineers| and backend teams to ensure models are reliable| observable| and safely embedded into production decision workflows at scale
Essential Skills: The ideal candidate has strong experience with ML frameworks (e.g.| TensorFlow| PyTorch| scikit-learn)| MLOps practices (model versioning| monitoring| retraining)| and understands explainability| bias| and guardrails in regulated environments.
Desirable Skills:
Keyword:
Skills: Digital : Machine Learning
Experience Required: 10 & Above