DevOps Engineer

• Infrastructure Development & Optimization: Architect and implement robust, scalable ML infrastructure that supports model training, deployment, and monitoring.
• ML Platform Engineering: Develop and maintain ML model serving and orchestration platforms, ensuring seamless integration with existing engineering workflows. Gitlab pipelines for software and machine learning engineering
• Data Pipeline and Feature Engineering: Design and optimize ETL/ELT pipelines for ML applications, enabling efficient and reliable data preprocessing and transformation.
• MLOps and Automation: Implement MLOps best practices to streamline model lifecycle management, from training to deployment, monitoring, and retraining.
• Cloud & Containerization: Leverage cloud computing resources (AWS, GCP) and container orchestration (Docker, Kubernetes) to scale ML workloads efficiently.
• Monitoring and Reliability: Develop advanced monitoring systems to track model performance, data drift, and infrastructure health.
• Security & Compliance: Collaborate with privacy and security teams to ensure compliance with regulatory standards and best practices for handling sensitive clinical data.
• Collaboration & Mentorship: Work closely with software engineers, data scientists, and ML engineers to align infrastructure with business and technical goals while mentoring junior engineers.
• Stay Current on Engineering and ML Infrastructure Trends: Keep up to date with advancements in ML platforms, distributed computing, and scalable ML systems, integrating innovative solutions into our ML ecosystem.

Staff SW Engineer, DevOps/MLOps

DevOps Engineer

Senior Site Reliability Engineer

Senior Site Reliability Engineer – Networking

Senior DevOps Engineer – Cloud Platform

Senior DevOps Engineer