Rackspace Technology is hiring a
Principal MLOPs Engineer, Remote - Canada

Summary

The job description is for a Principal ML OPS Engineer role at Rackspace Technology. The position involves architecting and optimizing an existing data infrastructure to support machine learning models, collaborating with cross-functional teams, and providing technical leadership. The role requires expertise in various areas such as deep learning frameworks, machine learning algorithms, computer science concepts, programming languages, remote work experience, and specific cloud services.

Requirements

• Proven track record in designing and implementing cost-effective and scalable ML inference systems
• Hands-on experience with leading deep learning frameworks such as TensorFlow, Keras, or Spark MLlib
• Solid foundation in machine learning algorithms, natural language processing, and statistical modeling
• Strong grasp of fundamental computer science concepts including algorithms, distributed systems, data structures, and database management
• Expert-level proficiency in at least one programming language such as Java, Python, or C++
• Ability to tackle complex challenges and devise effective solutions. Use critical thinking to approach problems from various angles and propose innovative solutions
• Worked effectively in a remote setting, maintaining strong written and verbal communication skills. Collaborate with team members and stakeholders, ensuring clear understanding of technical requirements and project goals
• Proven experience in Apache Hadoop ecosystem (Oozie, Pig, Hive, Map Reduce)
• Expertise in public cloud services, particularly in GCP and Vertex AI

Responsibilities

• Architect and optimize our existing data infrastructure to support cutting-edge machine learning and deep learning models
• Collaborate closely with cross-functional teams to translate business objectives into robust engineering solutions
• Own the end-to-end development and operation of high-performance, cost-effective inference systems for a diverse range of models, including state-of-the-art LLMs

Preferred Qualifications

A specialization in Machine Learning is preferred