Offer summary

Qualifications:

5+ years of experience in machine learning and data engineering., Hands-on experience with MongoDB/Atlas Data Lake and PyTorch., Strong knowledge of AWS SageMaker and MLOps principles., Proficiency in Python and ML frameworks like NumPy and pandas..

Key responsibilities:

Design and build end-to-end ML solutions for predictive failure modeling.

Develop robust and scalable ML pipelines using MongoDB and AWS SageMaker.

Collaborate with cross-functional teams to ensure efficient model deployment.

Utilize problem-solving skills to address challenges in ML pipeline development.

Job description

We are seeking a highly skilled Machine Learning Pipeline Expert to design and build end-to-end ML solutions focused on predictive failure modeling for our distributed and remote asset infrastructure. You will be instrumental in developing robust, scalable, and efficient ML pipelines, leveraging MongoDB/Atlas Data Lake for data storage and processing, and AWS SageMaker and PyTorch for model training, tuning, and deployment.

Requirements:

5+ years of experience in machine learning and data engineering, focused on ML pipeline development and deployment.
Hands-on experience with MongoDB / Atlas Data Lake, including querying, aggregation, and schema design.
Proficient in PyTorch for deep learning model development.
Strong working knowledge of AWS SageMaker, including pipeline orchestration, training jobs, and endpoint management.
Experience building ML pipelines for predictive maintenance, anomaly detection, or time-series analysis.
Proficiency in Python and ML frameworks (e.g., NumPy, pandas, scikit-learn).
Solid understanding of MLOps, DevOps principles, and tools (e.g., Git, Docker, CI/CD pipelines).
Excellent problem-solving and communication skills; ability to work independently and in cross-functional teams.

Required profile