Staff MLOps Engineer

Focused on building and operating critical systems for AI models in production, the full-time Staff MLOps Engineer will design robust ML infrastructure, optimize model deployment, and implement CI/CD processes in a remote setting. Key responsibilities Build ML infrastructure for low-latency model deployment and distributed inference pipelines Scale ranking systems by optimizing execution latency and cloud infrastructure costs Implement model CI/CD for automated versioning and zero-downtime rollbacks Required qualifications 4-8+ years of experience in MLOps, Machine Learning Engineering, or distributed platform engineering Hands-on experience with ultra-low-latency machine learning model deployment Proficiency in Python and PyTorch Experience with cloud platforms (AWS, GCP, or Azure) and containerization tools (Docker, Kubernetes) Experience designing scalable data pipelines and automated CI/CD infrastructures