AI Architect
Skills
About This Role
Role Overview
Design and deliver end-to-end AI solutions, with a strong focus on building business cases tailored to the sports, sports performance, education, and medical industries.
The role involves leveraging machine learning, natural language processing, and data analytics to develop scalable, high-impact AI systems that enhance operational efficiency and domain-specific outcomes.
AI Solution Architecture & Delivery
- Design and implement scalable AI architectures using cloud-native platforms such as Azure AI Foundry and Google Agent Space.
- Leverage pre-built AI services (NLP, computer vision, predictive analytics) to accelerate solution delivery.
- Align AI solutions with organizational priorities across sports, healthcare, and education sectors.
Enterprise System & Data Integration
- Integrate cloud-based AI services with enterprise systems such as ERP, CRM, medical, and sports performance platforms.
- Enable seamless connectivity between structured data (databases, KPIs) and unstructured data (documents, video, medical records).
- Utilize APIs, middleware, and automation tools to streamline data pipelines and workflows.
AI Governance & Risk Management
- Ensure compliance with data protection regulations such as GDPR, HIPAA, and relevant local laws.
- Monitor AI systems for bias, transparency, and explainability.
- Implement security, governance, and ethical AI best practices.
Applied AI Enablement
- Deploy and customize existing AI models for practical, real-world applications.
- Configure conversational AI, knowledge agents, and analytics tools to support business operations.
- Continuously evaluate advancements in AI platforms and implement enhancements.
Stakeholder & Client Management
- Collaborate with internal stakeholders to identify opportunities for AI adoption.
- Present AI solutions with clear, measurable business value.
- Act as a trusted advisor for AI-driven transformation initiatives.
Performance & Scalability
- Optimize AI systems for performance, scalability, and reliability.
- Establish monitoring frameworks and continuous improvement processes.
- Define KPIs and track ROI for AI implementations.
Qualifications & Experience
- Minimum 8+ years of experience in Solution Architecture.
- At least 4 years of hands-on experience in applied AI/ML projects.
- Mandatory: Bilingual (Arabic speaker)
Education
- Bachelor’s Degree in Computer Science, Artificial Intelligence, Data Science, IT, or Engineering.
Certifications
- AI-related certifications such as Microsoft Certified AI Developer/Architect.
- Experience with Azure AI Foundry and Google Cloud AI/ML platforms.
- Additional software development certifications (e.g., Microsoft Certified Developer/Architect) are a plus.
Required Skills & Competencies
- Strong expertise in designing and delivering AI-driven solutions.
- Proficiency in AI concepts, tools, frameworks, and models.
- Experience with solution architecture methodologies, including evaluation techniques, component modeling, and impact analysis.
- Solid understanding of SDLC, requirements analysis, and technical design (high-level and detailed).
- Hands-on experience with Azure AI Foundry for enterprise AI lifecycle management.
- Expertise in Google Agent Builder, SDKs for mobile agents, and multi-agent systems.
- Strong knowledge of Advanced Retrieval-Augmented Generation (RAG) for enterprise knowledge systems.
- Proficiency in data and database architecture (SQL, NoSQL, vector databases, knowledge graphs).
- Ability to design secure, scalable, and high-performance AI systems.
- Excellent communication skills to bridge technical and business stakeholders.
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