AI Architect
Role Overview
Design end-to-end AI solutions for complex business challenges across sports, sports performance, education, and healthcare.
Translate business requirements into scalable, secure, high-performing AI architectures that integrate with enterprise systems, with a focus on sports applications and performance optimization.
Key Skills for This Role
Full Job Posting
Role overview
Design end-to-end AI solutions for complex business challenges across sports, sports performance, education, and healthcare.
Translate business requirements into scalable, secure, high-performing AI architectures that integrate with enterprise systems, with a focus on sports applications and performance optimization.
Architecture and delivery
- Design and implement scalable AI architectures using cloud-native platforms such as Azure AI Foundry and Google Agent Space.
- Use pre-built AI services for NLP, computer vision, and predictive analytics to accelerate solution delivery.
- Align AI solutions with organizational priorities in sports, healthcare, and education.
Integration and governance
- Integrate cloud AI services with ERP, CRM, medical, and sports performance platforms.
- Connect structured and unstructured data sources, including databases, KPIs, documents, video, and medical records.
- Apply APIs, middleware, and automation tools to streamline data flows and pipelines.
- Ensure compliance with GDPR, HIPAA, and local data regulations.
- Monitor AI systems for bias, transparency, and explainability, and apply security and governance best practices.
Applied AI and performance
- Deploy and customize existing AI models for real-world use cases.
- Configure conversational AI, knowledge agents, and analytics tools to support operations.
- Evaluate cloud AI provider updates and implement improvements.
- Partner with stakeholders to identify AI adoption opportunities and demonstrate measurable business value.
- Optimize AI systems for speed, scalability, and reliability; implement monitoring and continuous improvement; define KPIs and track deployment ROI.
Qualifications
- At least eight years of experience in solution architecture, including at least four years in applied AI/ML projects.
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or Engineering.
- Bilingual Arabic speaker is mandatory.
Required skillsets
- Expertise developing AI projects and proficiency in AI concepts, tools, frameworks, and models.
- Proficiency in architecture evaluation, component modeling, impact analysis, SDLC, requirements analysis, and technical design.
- Proficiency in Azure AI Foundry for enterprise AI deployment and lifecycle management.
- Hands-on expertise with Google Agent Builder, Google SDK for mobile agents, and multi-agent systems.
- Strong knowledge of advanced RAG for enterprise-scale knowledge management.
- Database and data architecture skills across SQL, NoSQL, vector databases, and knowledge graphs.
- Ability to design secure, scalable, performance-optimized applied AI systems.
- Strong communication skills to bridge technical teams and business stakeholders.
Certifications
AI software development certifications such as Microsoft Certified AI Developer or Architect.
Azure AI Foundry and Google Cloud AI/ML platform certifications.
Software development certifications such as Microsoft Certified Developer or Architect.
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