Machine Learning Operations (MLOPs) Architect

Summary

Join Rackspace Technology as a seasoned Machine Learning Operations (MLOPs) Architect to build and optimize the ML inference platform. This remote position requires expertise in machine learning engineering and infrastructure, focusing on building and scaling ML inference systems in a production environment. You will architect and optimize data infrastructure, collaborate with cross-functional teams, and develop high-performance inference systems for various models, including LLMs. The role demands technical leadership, mentorship, and the ability to solve complex challenges with innovative solutions. A strong background in machine learning algorithms, natural language processing, and cloud services (particularly GCP and Vertex AI) is essential. This position offers a competitive salary range of $138,000 - $224,000 a year.

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, Pytorch, HuggingFace, Langchain, etc
• 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. 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
• Expertise in public cloud services, particularly in GCP and Vertex AI
• Proven experience in building and scaling Agentic AI systems in a production environment
• In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs
• Technical degree: Bachelor's degree in Computer Science with a minimum of 10+ years of relevant industry experience, or A Master's degree in Computer Science with at least 8+ years of relevant industry experience

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

Preferred Qualifications

A specialization in Machine Learning is preferred