Senior Software Developer - Oracle Health, Backend Focus

Oracle Oracle · Enterprise · United States

Senior Software Developer role focused on backend services and platform components for Oracle Health. The role emphasizes an 'AI-first engineering culture' where engineers are expected to use AI-assisted tools to enhance productivity and quality in development, testing, documentation, and operations. Responsibilities include designing and implementing backend services, end-to-end feature delivery, code reviews, production issue resolution, and collaboration. While AI tools are expected to be used, the core craft is traditional software development, not building AI models or systems.

What you'd actually do

  1. Design and implement backend services and APIs used by Oracle Health product teams.
  2. Deliver features end-to-end: requirements clarification, technical design, implementation, automated testing, deployment, and operational support.
  3. Participate in code and design reviews; contribute to engineering best practices (testing, CI/CD, observability, security).
  4. Diagnose and resolve production issues; participate in on-call and incident response and drive follow-up actions.
  5. Collaborate with cross-functional stakeholders (SRE/Operations, Security, Product, and other engineering teams).

Skills

Required

  • BS in Computer Science or related field (or equivalent practical experience)
  • 3+ years of professional software development experience
  • Proficiency in one or more languages (e.g., Java, C#, Go, Python)
  • experience building REST and/or gRPC services
  • Working knowledge of data stores (RDBMS and/or NoSQL)
  • distributed systems fundamentals
  • Experience with modern engineering practices: source control, code review, automated testing, CI/CD pipelines

Nice to have

  • AI-assisted approaches to accelerate delivery and improve quality across system design, coding, testing, documentation, and operations
  • Demonstrate practical experience using AI-assisted techniques/tools to improve developer productivity and quality (e.g., faster prototyping, stronger test coverage, safer refactoring, better documentation)
  • Apply an AI-first mindset to day-to-day work: generating and validating code suggestions, creating/maintaining tests, and improving observability and runbooks—while maintaining strong engineering judgment
  • Understand and follow enterprise security and privacy requirements when using AI tooling (e.g., protect sensitive data, use approved tools/workflows)

What the JD emphasized

  • AI-assisted approaches
  • AI-first engineering culture
  • AI-assisted techniques/tools
  • AI-first mindset