We are looking for a ML Engineer to work closely with the ML Architect to develop ML frameworks (TensorFlow, Scikit-Learn, Pytorch), experimentation platforms, and tools.
This role has the responsibilities to:
1. Develop large-scale distributed machine learning systems that are scalable, performant, efficient, and reliable.
2. Collaborate with cross-functional teams to deploy and integrate machine learning models.
3. Liaise with Business Units (BUs) for their ML needs and work on the cross-BU ML portfolio.
4. Optimize feature extraction, transformation, and selection.
5. Manage and work with Feature Stores for reusability across ML pipelines.
6. Ensure scalability, reliability, cost efficiency, and ease of use of the machine learning platform.
7. Contribute to evaluating and adopting new technologies and tools to enhance our machine-learning capabilities.
Requirements:
1. At least 5 years of experience as a Machine Learning Engineer.
2. Experience with ML frameworks (TensorFlow, Scikit-Learn, Pytorch).
3. Experience with model training, versioning, and monitoring.
4. Strong background in MLOps practices, including CI/CD, containerization (Docker), orchestration frameworks (Kubernetes, Airflow), model serving tools (AWS SageMaker, Databricks MLFlow), model observability frameworks, automation, and feature stores.
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