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Senior Cloud Solution Architect (OCI / Oracle Ecosystem)

Hyper Lychee Labs
Doha, QAT
Contract
Senior
Onsite
1 months ago
OCIOracle ERPAI/MLData ArchitectureKubernetesDevOps
Free

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OCIOracle ERPAI/ML
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Role Overview

  • Design an AI enabled MVP supporting request review in an enterprise environment.
  • Ingest internal and third party data to generate structured recommendations.
  • Enable decision support and approval routing through a lightweight workflow layer.
  • Define end to end architecture across data ingestion, cloud platform, AI/analytics, workflow, and reporting.
  • Work with business, IT, infrastructure, security, and data teams to confirm requirements.
  • Translate MVP into a deployable solution blueprint.

Requirements

  • Senior OCI technical architect with 8 12+ years in enterprise architecture.
  • Deep knowledge of OCI architecture including IAM, networking, compartments, compute, storage, API Gateway, Functions, OKE, and observability.
  • Proven experience with Oracle ERP integration, data extracts, APIs, and workflow triggers.
  • Strong background in data ingestion, transformation, storage, and orchestration using OCI Data Flow, Object Storage, Autonomous Database, and search services.
  • Familiarity with LLM enabled solution design, retrieval patterns, model orchestration, and embedding AI into enterprise workflows.
  • Ability to design for encryption, access control, auditability, private connectivity, and regulated data environments.
  • Experience shaping the experience layer across APIs, lightweight front ends, approval workflows, and dashboard/reporting integrations.
  • Practical understanding of GitHub, CI/CD, OCI DevOps, containerization, and production deployment patterns.
  • Comfortable working across IT, infrastructure, infosec, and business teams.

Key Responsibilities

  • Define the target OCI based architecture for the MVP.
  • Recommend what should sit in Oracle, OCI, and any adjacent cloud environments.
  • Design the ingestion approach for PR, PO, receiving, inventory, and third party data.
  • Define the deployment model for the AI and application layers.
  • Ensure security, scalability, and maintainability are built into the design.
  • Support technical decision making during MVP build and deployment.

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