Machine Learning Engineer is responsible for building scalable end-to-end data science solutions.

Build ML and statistics driven models and continuous model monitoring workflows.

Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust ML model lifecycle.

Scale and deploy holistic machine learning solutions after successful prototyping. 

Additional Skills & Qualifications

Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects.

Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.

Experience in scaling infrastructure to support high-throughput data-intensive applications using Scala/PySpark/GPU

Worked on integrating ML models with webservices using FastAPI or Flask.

Business Drivers/Customer Impact

The key business challenge mentioned in the meeting is the need for ML engineering resources with experience in building ML pipelines, taking models to production, and working on batch and real-time API deployments.

Why is the position open(provide details)

Large retailer in need of ML engineers to assist in building ML pipelines and taking models to productions. Have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects. Have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, Pytorch, SQL, Big Data, GCP, Shell scripting.