Oracle is seeking a Principal Data Systems Software Engineer (IC4) to design and build next-generation cloud-native and AI-powered capabilities for Oracle Database Cloud Platform. This role combines distributed systems engineering with modern AI application development, including Generative AI, LLMs, AI agents, and cloud-native architectures. The engineer will lead technical initiatives, influence architecture decisions, modernize platform capabilities, and develop highly scalable services running on Oracle Cloud Infrastructure.
Key Responsibilities
- Design, architect, and develop cloud-native services supporting Oracle Database on OCI.
- Build scalable AI-enabled platform capabilities leveraging LLMs, AI agents, RAG, and modern AI architectures.
- Design provisioning, lifecycle management, monitoring, automation, and operational frameworks for Database Cloud services.
- Collaborate with Product Management, Compute Engineering, Operations, and cross-functional Oracle engineering teams.
- Build production-grade distributed systems emphasizing scalability, resiliency, observability, and security.
- Modernize existing platform components into intelligent AI-first cloud-native services.
- Integrate enterprise systems, APIs, databases, and cloud services into AI-powered workflows.
- Troubleshoot production issues, perform root cause analysis, and provide Level 3 engineering support.
Qualifications & Skills Mandatory
Bachelor's or Master's degree in Computer Science or related discipline.
Strong experience building distributed systems and cloud-native applications.
Hands-on experience developing Generative AI and LLM-based applications.
Experience with:
- Retrieval-Augmented Generation (RAG)
- AI Agents
- Prompt Engineering
- Model Orchestration
- Vector Databases
- Embeddings
- AI Evaluation Frameworks
Production experience with AI/ML pipelines and inference services.
Experience with OCI, AWS, Azure, or GCP.
Kubernetes, Containers, REST APIs, Serverless technologies.
Strong Python and/or Java programming.
Experience with OpenAI SDKs, Hugging Face, or similar AI frameworks.
Microservices and event-driven architectures.
CI/CD, DevOps/MLOps, Infrastructure as Code (Terraform).
Enterprise integrations with scalability, observability, and security considerations.
Good to Have
- AI Copilots or Agentic AI platforms.
- AI observability platforms.
- Prompt lifecycle management.
- Guardrails and Responsible AI.
- MCP (Model Context Protocol).
- Knowledge graphs.
- Semantic Search.
- Database internals.
- Linux internals.
- Performance engineering.
- AI Governance.
- Multi-tenancy.
- Service Level Objectives (SLOs).
- 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?
Career Level - IC4