Oracle Database Cloud Platform powers Oracle Database-as-a-Service (DBaaS) across Oracle Cloud Infrastructure (OCI). We build the cloud platform that enables provisioning, lifecycle management, automation, observability, security, and operations for Oracle Database services across public, private, and hybrid cloud environments. Working closely with Product Management, OCI engineering, and Operations, we deliver highly scalable, enterprise-grade database cloud services used by customers worldwide.
Job Description
Cloud Service Development Engineer/ Principal Data Systems Software Engineer
Oracle is looking for an experienced Principal Data Systems Software Engineer to help shape the next generation of Oracle Database Cloud Platform. In this role, you will design and build cloud-native platform services while driving the adoption of AI-powered capabilities across Oracle's Database-as-a-Service offerings.
This is a unique opportunity to combine large-scale distributed systems engineering with modern AI technologies, including Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents. You will lead technical initiatives, influence platform architecture, and build intelligent services that simplify operations, improve developer productivity, and enhance customer experiences across Oracle Cloud Infrastructure.
If you enjoy solving complex distributed systems challenges while applying cutting-edge AI technologies to real-world cloud platforms, this role offers the opportunity to make a significant impact at enterprise scale.
Responsibilities
As a Principal Engineer, you will:
- Design, architect, and develop scalable cloud-native services for Oracle Database on Oracle Cloud Infrastructure (OCI).
- Build AI-powered platform capabilities using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern AI frameworks.
- Design and enhance provisioning, lifecycle management, automation, monitoring, observability, and operational workflows for Database Cloud services.
- Lead the modernization of existing platform components into intelligent, AI-driven cloud services.
- Build highly available, resilient, secure, and observable distributed systems operating at global scale.
- Integrate enterprise applications, cloud services, APIs, and databases into AI-powered workflows and automation.
- Collaborate with Product Management, OCI Compute, Operations, and cross-functional engineering teams to define and deliver platform capabilities.
- Drive technical architecture, mentor engineers, and promote engineering best practices.
- Troubleshoot complex production issues, perform root cause analysis, and provide Level 3 engineering support.
Basic Qualifications
- Degree in Computer Science or a related technical discipline.
- Extensive experience building distributed systems and cloud-native applications.
- Strong software development experience in Java and/or Python.
- Experience designing and building microservices and event-driven architectures.
- Hands-on experience developing and deploying production-grade Generative AI or LLM applications.
- Experience deploying applications on OCI, AWS, Azure, or Google Cloud Platform.
- Experience with Kubernetes, containers, REST APIs, and cloud-native development practices.
- Familiarity with CI/CD pipelines, Infrastructure as Code (Terraform), and DevOps/MLOps practices.
- Experience designing enterprise-scale systems with strong security, scalability, reliability, and observability.
Preferred Qualifications
Experience with one or more of the following is highly desirable:
AI & Machine Learning
- Large Language Models (LLMs)
- Retrieval-Augmented Generation (RAG)
- AI Agents / Agentic AI
- Prompt Engineering
- Model orchestration frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, etc.)
- Vector databases and embeddings
- AI evaluation and observability frameworks
- AI Copilots
- Guardrails and Responsible AI
- Model Context Protocol (MCP)
Cloud & Platform Engineering
- Distributed systems at enterprise scale
- Kubernetes and container orchestration
- Serverless architectures
- Enterprise integrations
- Multi-tenant platform design
- Performance engineering
- Service Level Objectives (SLOs)
- Observability and monitoring
- Database internals
- Linux systems programming
Nice to Have
- Knowledge graphs
- Semantic search
- AI governance
- Enterprise workload modernization
Self-Assessment Questions:
- Have I built production-grade Generative AI or LLM applications
- Have I designed distributed cloud-native systems running at enterprise scale
- Am I comfortable architecting AI applications using RAG, AI Agents, and Vector Databases
- Have I deployed AI models and production inference pipelines using Kubernetes or cloud platforms
- Can I independently design scalable microservices while mentoring other engineers
