Lead Machine Learning Engineer (MLOps, PyTorch, AWS)

- Bachelors degree - Minimum of 6 years of experience in designing and developing data-intensive solutions using distributed computing (Internship experience is not applicable) - At least 4 years of programming experience with Python, Scala, or Java - A minimum of 2 years of experience building, scaling, and optimizing machine learning systems - Masters or doctoral degree in computer science, electrical engineering, mathematics, or a related field (preferred) - 3+ years of experience in creating production-ready data pipelines for machine learning models (preferred) - 3+ years of practical experience with established machine learning frameworks such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow (preferred) - 2+ years of experience in developing high-performance, resilient, and maintainable code (preferred) - 2+ years of experience in data collection and preparation for machine learning models (preferred) - 2+ years of leadership experience (preferred) - 1+ years of experience guiding teams in developing machine learning solutions using industry best practices (preferred) - Exposure to developing and deploying machine learning solutions in public cloud environments such as AWS, Azure, or Google Cloud Platform (preferred) - Experience in designing, implementing, and scaling complex data pipelines for machine learning models and assessing their performance (preferred) - Demonstrated impact in the machine learning field through conference presentations, publications, blog posts, open source contributions, or patents (preferred)