U

AI Infrastructure Engineer

Utilidata • United States
Remote
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Design, build, and own end-to-end infrastructure serving Utilidata's AI and ML models across edge, cloud, and data center environments. Lead the AI inference platform, GPU optimization, and MLOps practices for production AI capabilities. Requires deep expertise in ML model serving, distributed systems, and GPU infrastructure with cross-functional collaboration.

Key Highlights
Fully remote with periodic travel for retreats and on-site engagements
First dedicated AI infrastructure role, foundational for production AI deployment
Cross-functional work with product, engineering, and data science teams
Key Responsibilities
Lead the design and build of Utilidata's AI inference platform
Own end-to-end model serving infrastructure (on-prem and datacenter)
Build and maintain fault-tolerant, high-performance systems for serving AI models at scale
Collaborate with algorithms engineers to integrate AI inference data with power optimization algorithms
Optimize GPU utilization and inference performance across hardware fleet
Establish MLOps best practices including CI/CD pipelines, monitoring, and rollback
Contribute to infrastructure roadmap decisions including build vs. buy tradeoffs
Technical Skills Required
Machine Learning Distributed Systems GPU

Job Description

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically orchestrate power and unlock more compute capacity from existing energy infrastructure. For over a decade, we have applied AI to the electric grid - bringing real-time visibility and power-flow control to complex energy infrastructure. Our Karman platform, built on a custom NVIDIA module, brings that same capability to AI data centers, giving operators a way to better use the power already available to them.

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