Design and architect end-to-end AI solutions that address complex business challenges across the sports, sports performance, education, and healthcare sectors. Develop compelling AI business cases by identifying high-value use cases and leveraging machine learning, natural language processing, computer vision, and advanced data analytics.
Translate business requirements into scalable, secure, and high-performing AI architectures that integrate seamlessly with existing enterprise systems, with a particular focus on sports-specific applications and performance optimization.
Responsibilities
AI Solution Architecture & Delivery:Design and implement scalable AI architectures using cloud-native platforms (Azure AI Foundry, Google Agent Space). Leverage pre-built AI services (NLP, computer vision, predictive analytics) to accelerate solution delivery. Align AI solutions with organizational priorities in sports, healthcare, and education.
Enterprise System & Data Integration:Integrate cloud AI services with ERP, CRM, medical, and sports performance platforms. Enable connectivity between structured (databases, KPIs) and unstructured (documents, video, medical records) data sources. Apply APIs, middleware, and automation tools to streamline data flows and pipelines.
AI Governance & Risk Management:Ensure compliance with GDPR, HIPAA, and local data regulations. Monitor AI systems for bias, transparency, and explainability. Apply security and governance best practices in AI deployment.
Applied AI Enablement:Deploy and customize existing AI models for real-world use cases. Configure conversational AI, knowledge agents, and analytics tools to support operations. Evaluate updates from cloud AI providers and implement improvements.
Stakeholder & Client Management:Partner with internal stakeholders to identify opportunities for applied AI adoption. Demonstrate practical AI solutions with measurable business value. Act as a trusted advisor for AI-enabled transformation
Performance & Scalability:Optimize applied AI systems for speed, scalability, and reliability. Implement monitoring and continuous improvement mechanisms. Define KPIs and track ROI of AI deployments
Qualifications
8+ years of experience in Solution Architecture with minimum of 4 years of experience in applied AI/ML projects. Bachelor's Degree in Computer Science Or Artificial Intelligence, Data Science or Engineering. Bilingual (Arabic Speaker) Mandatory
Certifications
AI Software development related certifications such as MS Certified AI Developer or Architect Azure AI Foundry and Google Cloud AI/ML platforms Software development related certifications such as MS Certified Developer or Architect
Required Skillsets:
Expertise in developing AI projects. Proficient in AI concepts, tools, frameworks and models. Proficient in multiple solution architecture techniques such as conducting architecture evaluation using scenarios, component modelling and impacts analysis Proficient in SDLC, requirements analysis, high level and detailed 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. Skilled in database and data architecture (SQL, NoSQL, vector databases, knowledge graphs). Ability to design applied AI systems that are secure, scalable, and performance-optimized. Strong communication skills to bridge technical teams and business stakeholders.