MLOps Engineer

Job Summary

To build and scale the inference infrastructure for generative audio models, the full-time MLOps Engineer will design and deploy high-performance systems for low-latency model serving while working remotely.

Key responsibilities

• Design and maintain inference infrastructure for generative audio model architectures
• Implement and manage high-performance inference engines and orchestrate service deployments using Kubernetes (K8S)
• Develop and automate robust CI/CD pipelines to streamline testing and deployment of model artifacts and configurations

Required qualifications

• Deep understanding of modern audio model architectures (e.g., TTS, ASR) and their inference requirements
• Strong hands-on experience with Kubernetes (K8S) and implementing autoscaling strategies for production workloads
• Solid background in MLOps, including CI/CD automation and managing scalable cloud infrastructure
• Proficiency in Python or Go for infrastructure tooling and backend services
• Experience with GPU-accelerated inference and performance profiling techniques