Building on our Cloud momentum, Oracle has established a new organization: Oracle Health & AI. This team concentrates on product development and strategy to construct a modernized healthcare system based on AI technology. This is a fresh line of business, forged with an entrepreneurial spirit that cultivates an energetic and creative environment.  We are unencumbered and will rely on your contribution to transform it into a world-class engineering center with an unwavering focus on excellence.

As a Senior Backend Engineer, you will play a vital role in shaping the future of Health and AI at Oracle. As a member of the Clinical Digital Assistant Team, you will spearhead efforts in areas such as service resiliency, security, performance, scalability, networking, and automation. Duties and tasks are diverse and intricate, demanding independent judgment.

Qualifications and Experience

BS or MS in Computer Science, or equivalent experience 3+ years of working experience shipping scalable, cloud-native distributed systems Proficient in at least one of the languages: Java, Python, Terraform Solid understanding of user management and runtime security You are experienced at building highly available services, possessing knowledge of common service-oriented design patterns and service-to-service communication protocols Experience in cloud computing (Compute, Networking, Storage, etc) Experience in container orchestration like Kubernetes/Docker/Helm Charts Ability to work in a collaborative, cross-functional team environment Experience with production operations and best practices for putting quality code in production and troubleshooting issues when they arise Able to effectively communicate technical ideas verbally and in writing (technical proposals, design specs, architecture diagrams, and presentations)

Preferred Qualifications

Knowledge of healthcare and experience delivering healthcare applications are significant plus.

Responsibilities

Collaborate with product managers and service teams to build secure, scalable infrastructure that can support the growing needs of our AI service offerings. Implement robust data pipelines for collecting, processing, and transforming large-scale datasets. Stay up-to-date with the latest advancements in machine learning infrastructure, tools, and technologies, and evaluate their potential impact on our systems. Deploy and manage machine learning models in production environments, ensuring reliability, scalability, and security. Coordinate with multinational teams to drive projects from research POC to production. Develop production code and advocate for the best coding and engineering practices. Identify and mitigate risks in our plans and executions, especially at the intersection of business and engineering.