• Apply machine learning, statistics, to develop algorithms • Solve challenging problems, signal processing, and computer networking domains • Leverage strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain. • Proficiency in designing and building data pipelines, including experience with ETL processes and data warehousing solutions • Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment • Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) • Establish ML governance practices, including version control for datasets and models