Senior DevOps Engineer (MLOps, Infrastructure)

Shape Our MLOps Infrastructure - Design, implement, and maintain MLOps infrastructure on GCP (Terraform, Pub/Sub, Vertex AI, CI/CD, GitHub Actions) to ensure scalability, automation, and reliability. - Act as a Subject Matter Expert for MLOps and infrastructure, setting best practices and guiding the team in deploying and maintaining ML models in production. - Build and optimize CI/CD pipelines for efficient training, serving, and monitoring of ML models, ensuring seamless operations at scale. - Work within the ML engineering team, focusing on productionizing ML models, optimizing inference pipelines, and ensuring long-term model stability. - Serve as the bridge between the ML team and the software development team, ensuring smooth integration of infrastructure and ML workflows. - Take part in core ML engineering tasks, such as managing model inference pipelines, monitoring performance, and ensuring model stability in production. - Contribute hands-on with Python development, focusing on automation, infrastructure, and tooling. Our tech stack includes: - Infrastructure (GCP, Vertex AI, Cloud Run, Terraform, GPU, Docker, GitHub Actions, CI/CD) - Python and computer vision libraries (e.g. NumPy, pandas, OpenCV, scikit-image) - Frameworks associated with training and evaluation of image recognition tasks (PyTorch) - Databases (SQL, BigQuery)