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The DataOps/ML Engineer will develop high-quality applications using Python and AWS services, including AWS SageMaker. Responsibilities include building and monitoring ML models, using container technologies like Docker and Kubernetes, and automating deployment pipelines. Additionally, the engineer will collaborate on various projects, contributing to significant client outcomes in a dynamic consulting environment.

In this role, you will contribute to developing high-quality applications within a collaborative team. Responsibilities include coding in Python, utilizing AWS services for machine learning, and managing deployment pipelines with various tools and technologies.

The DataOps/ML Engineer will work with a variety of technologies to develop high-quality applications, contribute to machine learning automation, and monitor ML models. Responsibilities include collaborating on projects, utilizing AWS services, and improving deployment pipelines.

Join Bluelight Consulting as a DataOps/ML Engineer, where you'll develop high-quality applications using Python, AWS, and machine learning tools. Collaborate with clients on challenging projects in a dynamic team environment, focusing on software development, automation, and deployment pipelines.

The DataOps/ML Engineer will contribute to developing high-quality applications, utilizing skills in Python and AWS, and working with machine learning tools like TensorFlow and PyTorch. Responsibilities include automating deployment pipelines, managing ML monitoring tools, and collaborating on projects that enhance user experiences.

The role involves developing high-quality applications, focusing on machine learning and automation. Responsibilities include coding in Python, managing AWS services, utilizing ML frameworks, and working with various deployment tools and infrastructure technologies. Candidates will collaborate on projects, enhancing user experience while fostering a continuous learning environment.

As a DataOps/ML Engineer, you will contribute to developing high-quality applications using AWS services and machine learning tools, ensuring effective automation and monitoring of ML pipelines, while collaborating in a dynamic team environment.