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Cloud Service Development Engineer

Job Description - Cloud Service Development Engineer

Description

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.



Responsibilities
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?



Qualifications

Career Level - IC4



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