To support the development of AI-powered pathology solutions, the full-time Senior Software Engineer, MLOps will design, develop, and scale machine learning infrastructure while collaborating with cross-functional teams in a hybrid work environment. Key responsibilities Architect and build infrastructure and automation in AWS and on-premises for ML application development and deployment Drive system design and lead architectural discussions for the MLOps suite, ensuring compliance with performance and security standards Optimize ML workflows and automate operations, including CI/CD for ML models and deployment strategies using tools like Kubernetes and Airflow Required qualifications BS or Master's in Computer Science, Computer Engineering, Software Engineering, or a closely related field 5+ years of software engineering experience focused on building production-grade frameworks or applications Strong skills in complex, multi-language systems and scalable backend architecture Experience with Kubernetes and cloud computing platforms, preferably AWS Proficiency in Python and familiarity with observability and monitoring tools (e.g., Prometheus, Grafana, Datadog) Experience: 5 years required