Senior Machine Learning Infrastructure Engineer

2 weeks ago


San Antonio, Texas, United States Rackspace Full time
About the Role

We are seeking a seasoned Principal ML OPS Engineer to design, build, and optimize ML inference platforms. The ideal candidate will have significant expertise in Machine Learning engineering and infrastructure, with a focus on building high-performance, cost-effective inference systems.

Key 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.
  • Provide technical leadership and mentorship to foster a high-performing engineering team.
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.
  • Ability to tackle complex challenges and devise effective solutions.
Preferred Qualifications
  • Proven expertise in applying model optimization techniques (distillation, quantization, hardware acceleration) to production environments.
  • Proficiency and recent experience in Java is required.
  • In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs.
About Rackspace Technology

We are the multicloud solutions experts. We combine our expertise with the world's leading technologies to deliver end-to-end solutions. We have a proven record of advising customers based on their business challenges, designing solutions that scale, building and managing those solutions, and optimizing returns into the future.



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