Rackspace Us Inc.
DevOps Engineer IV - US
US-Work from Home · Senior
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About the role
- About Rackspace Technology Rackspace Technology is the trusted operator of the full enterprise AI stack, from governed private cloud to AI inference and agents in production.
- We bring our whole selves to work every day.
Responsibilities
- 1+ year informally mentoring engineers or leading design reviews.
- Advanced expertise in cloud infrastructure, containerization/orchestration, and IaC; independently designs highly available, cost-optimized architectures.
- Independently designs CI/CD systems and deployment pipelines, including LLMOps pipelines for AI/ML models.
- Deep understanding of AI infrastructure operational signals (latency, token cost, drift) and designs monitoring/alerting for them.
- Mentors mid-level and junior engineers on infrastructure design and secure AI-endpoint practices.
- Architect and own end-to-end delivery of complex infrastructure and CI/CD systems, including LLMOps pipelines, spanning multiple services.
- Lead design reviews and set technical direction for own area; mentor junior/mid engineers.
- Lead incident response and root-cause analysis for high-impact infrastructure issues within own domain.
- Participate in the on-call rotation for after-hours and weekend support.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent experience.
- 6-9 years of experience in technology, with significant infrastructure ownership.
- Deep experience with LLMOps at scale (GPU provisioning, vector DB hosting
Skills
- Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience.
Compensation
- Our compensation reflects the cost of labor across several geographic markets.
Company info
- And we embrace the notion that unique perspectives fuel innovation and enable us to best serve our customers and communities around the globe.
This listing is sourced directly from Rackspace Us Inc.'s careers page and normalized into a canonical job model.