Machine Learning Engineer, Ops

We are looking for an MLOps Engineer to build and scale the inference infrastructure for our generative audio models, including Text-to-Speech (TTS), voice conversion, and Automatic Speech Recognition (ASR). You will be responsible for designing and deploying high-performance systems that ensure low-latency, reliable, and scalable model serving for both streaming and batch inference. This role is central to bridging the gap between research and production, ensuring our audio models are optimized

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