AI Architect
Black & Grey HR
Black & Grey HR is recruiting for an established technology solutions and services provider in Doha, Qatar. Our client is seeking an experienced AI Architect to design and deliver scalable, enterprise-grade AI solutions across sports, sports performance, education, and medical applications. The role will focus on applied AI, solution architecture, enterprise integration, AI governance, and the practical adoption of AI technologies to deliver measurable business value.
Key Responsibilities:
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 including NLP, computer vision, and predictive analytics to accelerate solution delivery.
- Design AI solutions aligned with organizational priorities across sports, healthcare, education, and sports performance.
- Develop end-to-end AI architectures covering solution design, integration, deployment, scalability, and lifecycle management.
- Evaluate architecture options using scenarios, component modelling, impact analysis, and technical trade-offs.
Enterprise System & Data Integration
- Integrate cloud AI services with ERP, CRM, medical, sports performance, and other enterprise platforms.
- Enable connectivity between structured data sources including databases and KPIs and unstructured data such as documents, video, and medical records.
- Design APIs, middleware, and automation mechanisms to streamline enterprise data flows and pipelines.
- Develop data and AI architectures supporting SQL, NoSQL, vector databases, and knowledge graphs.
- Ensure AI solutions integrate effectively with existing enterprise technology and data environments.
Applied AI & Intelligent Solutions
- Deploy and customize existing AI models for real-world business and operational use cases.
- Develop and configure conversational AI, knowledge agents, analytics solutions, and AI enabled applications.
- Apply machine learning, natural language processing, computer vision, predictive analytics, and other AI capabilities to practical use cases.
- Implement Advanced RAG architectures for enterprise-scale knowledge management.
- Design and support multi-agent systems and AI agent solutions using relevant cloud platforms and SDKs.
- Evaluate updates and new capabilities from cloud AI providers and identify opportunities for practical adoption.
- Support the integration and continuous improvement of AI solutions across business functions.
AI Governance & Risk Management
- Ensure AI solutions comply with GDPR, HIPAA, and applicable local data protection and regulatory requirements.
- Apply security and governance best practices throughout the AI solution lifecycle.
- Support responsible AI practices covering transparency, explainability, and bias monitoring.
- Design AI solutions with appropriate security, privacy, access controls, and governance mechanisms.
- Identify and address AI-related technical, operational, security, and compliance risks.
Performance, Scalability & Continuous Improvement
- Optimize AI systems for performance, scalability, reliability, and operational efficiency.
- Implement monitoring mechanisms to track AI system performance and availability.
- Define KPIs and success measures for AI deployments.
- Track business value, operational performance, and ROI of AI solutions.
- Continuously improve AI architectures based on performance data, business requirements, and emerging technology capabilities.
Stakeholder & Client Management
- Partner with internal stakeholders to identify opportunities for applied AI adoption.
- Work closely with business and technical teams to understand requirements and translate them into scalable AI solutions.
- Demonstrate practical AI solutions and communicate their business value to stakeholders.
- Act as a trusted advisor on AI-enabled transformation and technology adoption.
- Bridge technical teams and business stakeholders through clear communication and solution recommendations.
Requirements
- 8+ years of experience in Solution Architecture, with a minimum of 4 years of experience delivering applied AI/ML projects.
- Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Engineering, or a related discipline.
- Strong experience developing and delivering AI projects in enterprise environments.
- Strong understanding of AI concepts, tools, frameworks, models, and applied AI technologies.
- Strong knowledge of solution architecture techniques including architecture evaluation, scenario analysis, component modelling, and impact analysis.
- Strong understanding of SDLC, requirements analysis, high-level architecture, 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.
- Strong understanding of database and data architecture including SQL, NoSQL, vector databases, and knowledge graphs.
- Ability to design secure, scalable, reliable, and performance-optimized applied AI systems.
- Strong communication and stakeholder management skills.
Required AI & Technology Skills- Azure AI Foundry and enterprise AI deployment.
- Google Agent Builder and Google AI/agent technologies.
- Multi-agent systems and agent-based architectures.
- Advanced RAG and enterprise knowledge management.
- Machine Learning and AI model deployment.
- Natural Language Processing and conversational AI.
- Computer Vision and predictive analytics.
- SQL and NoSQL databases.
- Vector databases and knowledge graphs.
- APIs, middleware, enterprise integration, and data pipelines.
- AI solution architecture and technical design.
- AI security, governance, transparency, explainability, and compliance.
- Performance monitoring, scalability, reliability, and continuous improvement.
Certifications
AI and software development related certifications would be preferred, including:
- Microsoft Certified AI Developer or AI Architect certifications.
- Azure AI Foundry related certifications or credentials.
- Google Cloud AI/ML platform certifications.
- Microsoft Certified Developer or Architect certifications.
- Other relevant AI, cloud, software architecture, or AI engineering certifications.
Preferred Experience
- Experience designing AI solutions for sports, sports performance, healthcare, medical, or education environments.
- Experience integrating AI platforms with ERP, CRM, medical, and sports performance systems.
- Experience working with structured and unstructured enterprise data, including documents, video, and medical records.
- Experience with enterprise-scale Advanced RAG and knowledge management solutions.
- Experience designing multi-agent and conversational AI solutions.
- Experience with cloud-native AI architectures and AI lifecycle management.
- Experience working with highly regulated data environments and privacy requirements.
Benefits
- Competitive Salary + Benefits Package
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