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The Principal ML Ops Engineer will design, implement, and scale ML infrastructure, focusing on optimizing AI systems, collaborating with teams, and establishing best practices for ML Ops.

Owner/implementer of end-to-end full stack features for data-intensive AI products: design and build scalable Node.js backends, REST APIs, and React+TypeScript frontends; optimize performance; establish testing, observability, and CI/CD; collaborate with product, design, and ML teams; debug and support production systems.

You will design, implement, and optimize NVIDIA CUDA kernels for AI systems, improve GPU memory pipelines, and profile workloads to enhance performance.

The AI Infrastructure Engineer will build and manage scalable model serving infrastructure for AI/ML applications, ensuring high efficiency and reliability in production.