Job Description

The Software Assurance Group at Oracle is seeking a talented Senior Software Developer to join our machine learning engineering team, which works at the forefront of enabling secure, scalable, and highly available AI-powered solutions. In this role, you will engage closely with fellow engineers and stakeholders, including ML engineers, architects, and product managers, to design, develop, and deliver innovative tools and cloud-native applications that ensure operational excellence for large-scale, global AI systems.



Our team develops products that support the secure transport and processing of ML artifacts, scalable telemetry pipelines for real-time monitoring, and orchestration frameworks to efficiently manage cloud infrastructure resources for dynamic, multi-tenant AI workloads. You will work hands-on across the software engineering lifecycle, driving solutions from design through implementation, testing, deployment, and ongoing operation.

Key Responsibilities:

Design and deliver robust, scalable, and secure cloud-native features with end-to-end ownership, including development, testing, operational excellence, and continuous improvement. Resolve complex technical issues and influence architectural decisions for distributed, multi-platform solutions. Collaborate cross-functionally with technical leads, engineering management, product managers, and architects to ensure timely, high-quality delivery of features. Proactively identify and mitigate project risks and blockers. Support integration efforts for external application teams and guide them on best practices. Stay up-to-date with latest Oracle Cloud technologies and continuously evolve our provisioning and enablement processes. Mentor and support junior team members, fostering technical growth and a culture of excellence.

Required Qualifications:

BS in Computer Science or related technical fields 4+ years of software engineering experience, including direct exposure to at least one major cloud service provider (OCI, AWS, Azure, or GCP). Deep understanding of distributed systems architecture, with a focus on fault tolerance and high availability. Hands-on experience designing and building microservices and cloud-native applications. Proficiency in Python and at least another modern programming language (Go, Java, Kotlin, or C/C++) Experience in containers and orchestration frameworks (Docker, Kubernetes). Excellent problem-solving skills, strong communication capabilities, and detail-oriented approach. Working knowledge of observability and monitoring tools (Prometheus, Grafana), CI/CD pipelines (Jenkins, GitLab), and build tools (Gradle, Maven, or similar). Understanding of core machine learning concepts and workflows to support ML engineering initiatives. Demonstrated ability to work both independently and collaboratively in a fast-paced environment with minimal supervision.

Preferred Qualifications:

MS in Computer Science or related technical fields. Familiarity with architectural patterns for high availability, scale-out, disaster recovery, and security in cloud environments. Experience designing or maintaining telemetry and metrics systems, and visualization dashboards using modern tools. * Prior experience with high-throughput distributed systems or data pipelines.