ML Data Engineer

Design, build, and maintain scalable data pipelines and feature stores for end-to-end ML workflows on the NBA platform.,Collaborate with ML researchers and software engineers to productionize models and translate experimental approaches into reliable systems.,Develop and deploy AI agents for workflow automation and process reduction.,Manage the ML data lifecycle: ingestion, validation, feature engineering, deployment, monitoring, and optimization.,Ensure data quality, reliability, governance, and performance at scale for real-time client experiences.,Proficiency in Python and Java, with hands-on experience in modern data and ML tooling (Spark, Airflow, feature stores, ML platforms).,Strong foundation in data and software engineering, designing scalable data pipelines and ML-ready datasets in hybrid environments (on-prem and public cloud like AWS).,Solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring.,Experience building or enabling AI-driven automation (agents, workflow orchestration, decision engines).,Experience with DevOps and CI/CD tooling (Jenkins, GitHub Actions) for automating testing, builds, and deployments for data and ML pipelines.

Bachelor's degree in Computer Science, Software Engineering, or a related field.

Bachelor's Degree