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The DataOps/ML Engineer will develop high-quality applications, utilizing advanced knowledge of AWS services and machine learning frameworks. Responsibilities include coding in Python, working with ML monitoring tools, and managing infrastructure with container technologies. The role fosters collaboration and is geared towards driving continuous professional growth.

The DataOps/ML Engineer will develop high-quality applications, work with AWS services and various ML frameworks, manage ML monitoring and automation tools, and utilize infrastructure technologies while promoting continuous learning in a collaborative environment.

The DataOps/ML Engineer will develop high-quality applications and contribute to meaningful projects in a fast-paced environment. They will leverage their strong programming skills and AWS knowledge to work with ML monitoring tools and deployment pipelines, enhancing software consultancy offerings.

As a DataOps/ML Engineer at Bluelight Consulting, you will develop high-quality applications using Python and AWS services, work with ML tools and automation technologies, and contribute to various projects for diverse clients to promote professional growth in a collaborative environment.

The DataOps/ML Engineer will contribute to the development of high-quality applications, focusing on machine learning services and deployment automation. Responsibilities include coding in Python, utilizing AWS services, working with ML frameworks, and managing deployment pipelines and infrastructure technologies.

The DataOps/ML Engineer will develop high-quality applications, focusing on machine learning and automation tools. Responsibilities include leveraging AWS services, monitoring ML processes, and working with container and infrastructure technologies. The role encourages professional growth in a collaborative environment.

As a DataOps/ML Engineer at Bluelight Consulting, you will develop high-quality applications in a collaborative environment. Duties include leveraging AWS services, utilizing machine learning frameworks like TensorFlow and PyTorch, and automating deployment pipelines with tools like GitLab CI/CD and Jenkins.