Machine Learning Operations (MLOPs) Architect - GCP - (US Remote)

Rackspace, a leading multicloud solutions provider, is seeking an experienced Machine Learning Operations (MLOPs) Architect to join their team in a remote capacity. This role represents an exciting opportunity to shape the future of ML infrastructure and deployment at a company known for its technical excellence and innovative approach to cloud solutions.

The position calls for a seasoned professional with deep expertise in Machine Learning engineering and infrastructure, particularly in building and scaling ML inference systems. The ideal candidate will bring 10+ years of hands-on experience (or 8+ years with a Master's degree) in computer science, with a strong focus on machine learning and distributed systems.

As an MLOps Architect, you'll be at the forefront of architecting and optimizing data infrastructure for cutting-edge machine learning and deep learning models. Your responsibilities will span from designing cost-effective inference systems to providing technical leadership for the engineering team. The role requires expertise in Java programming, deep learning frameworks, and cloud platforms, particularly GCP and Vertex AI.

The position offers competitive compensation ranging from $143,700 to $245,520 depending on location and experience. Working remotely, you'll be part of a company consistently recognized as a best place to work by Fortune, Forbes, and Glassdoor. This role provides an excellent opportunity to work with state-of-the-art technologies, including LLMs, while contributing to innovative solutions that help customers embrace technology and deliver the future.

The ideal candidate will possess strong communication skills, critical thinking abilities, and a proven track record of working effectively in remote settings. You'll be joining a culture that values diverse perspectives and fosters innovation, working alongside talented professionals who are passionate about delivering cutting-edge cloud solutions to customers worldwide.