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Data & AI Architect

Xebia
Dubai, UAE
Full-time
Mid-Senior
Onsite
Discovered 1 weeks ago
Data and AI architectureArchitectural designSoftware developmentPythonJavaAPIs and microservices

Full Job Posting

About Xebia

Xebia describes itself as a global AI-first consulting, software engineering, and training company helping organizations turn AI strategies into production-ready solutions.

The company describes a global delivery model with client-facing teams, nearshore capacity, and an offshore hub, supporting scalable digital and AI transformation.

Role and work arrangement

The full-time Data & AI Architect role is based in Dubai, United Arab Emirates, with a hybrid arrangement that allows some work from home.

The role designs end-to-end data and AI architectures, including data pipelines, model deployment frameworks, and integration patterns for enterprise-scale solutions.

Responsibilities

  • Collaborate with stakeholders to understand business requirements and translate them into technical architectures.
  • Guide software development teams and ensure solutions are secure, scalable, and maintainable.
  • Select tools and platforms, define architectural standards and best practices, and oversee integration with existing systems and cloud environments.
  • Support project planning, provide technical leadership during delivery, and mentor team members on modern data, cloud, and AI engineering practices.

Qualifications

  • Strong architecture and architectural design skills for scalable data and AI solutions and enterprise systems.
  • Hands-on software development experience in relevant languages and frameworks, such as Python, Java, or cloud-native services.
  • Proficiency in integration, including APIs, microservices, event-driven architectures, and enterprise system connections.
  • Project management capabilities including technical delivery leadership, effort estimation, risk management, and agile cross-functional work.
  • Experience with data platforms and AI technologies, including cloud data warehouses, data lakes, ML/AI platforms, MLOps, and model lifecycle management.
  • Knowledge of major cloud providers such as AWS, Azure, or GCP and their data and AI services; ability to design secure, cost-effective cloud architectures.
  • Excellent communication and stakeholder management skills, including explaining technical concepts to non-technical audiences.
  • Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent practical experience.

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