Principal Application Software Engineer
Position Overview
Oracle Government Defense & Intelligence is seeking a Principal Application Software Engineer to design, build, and operate secure application services for sovereign and government cloud environments. This role is a senior technical contributor who leads complex design and implementation work, guides engineers, improves application development practices, and resolves complex production and integration issues across distributed systems. This role will serve as a technical lead for AI-enabled software capabilities, including secure application architecture, governed data integrations, evaluation tooling, and production-ready operational workflows.
Who We Are
Oracle Sovereign Cloud builds and operates secure cloud capabilities for mission-critical government workloads. Our engineers build resilient services, automation, and integrations that support cloud operations in highly secure environments.
Key Responsibilities
Lead design and implementation of application features, services, APIs, and integrations aligned to secure, scalable architecture.
Develop and guide new application features and enhancements, including design specifications, implementation plans, and technical documentation.
Lead code reviews across multiple application areas and coach engineers on quality, maintainability, security, and operational readiness.
Troubleshoot and resolve complex application, API, service-to-service, integration, and production issues.
Contribute to architecture decisions, scalability strategies, performance optimization, and periodic technical reviews.
Implement comprehensive testing, observability, monitoring, error logging, and debugging practices.
Collaborate with product, program, QA, operations, security, and customer-facing teams to align technical delivery with mission and business needs.
Mentor engineers and contribute to hiring, technical standards, and continuous improvement across the team.
Design and implement AI-enabled software capabilities, including secure service integrations, workflow orchestration, evaluation tooling, operational data access, and production application features.
Define engineering patterns for testing, validating, monitoring, and operating AI-enabled software, including reliability, latency, quality, failure handling, auditability, and safe degradation.
Lead technical design for governed access to approved operational data sources and ensure AI-enabled features provide evidence-grounded, supportable outputs.
Mentor engineers on secure implementation, observability, debugging, and operational readiness for AI-enabled software in sovereign or restricted environments.
Minimum Qualifications
U.S. Citizenship with active US Government security clearance, TS/SCI with Poly, at hire.
11 years of software development experience; or bachelor's degree in a relevant technical field and 7 years of software development experience; or master's degree in a relevant technical field and 5 years of software development experience; or doctorate in a relevant technical field and 3 years of software development experience.
6 years of experience with programming and/or scripting languages.
4 years of experience with databases.
Experience designing, building, testing, and operating distributed application services.
Demonstrated experience with code review, automation, quality assurance, secure development practices, and technical mentorship.
Preferred Qualifications
11 years of software development experience or equivalent advanced degree plus experience.
3 years of experience with cloud platforms such as Oracle Cloud Infrastructure, AWS, Azure, or Google Cloud.
1 year of experience working with operating budgets and/or project financials.
Experience with RESTful APIs, microservices, service-oriented architecture, CI/CD, containers, Kubernetes, Linux, observability, and incident response.
Experience delivering software in regulated, classified, sovereign, or air-gapped environments.
Experience building production AI-enabled software, including secure application integrations, evaluation frameworks, workflow automation, model-serving integrations, operational data platforms, or observability for AI-enabled systems.