AI Architect

AI Architect at JODAYN in الرياض, S01, SA. This Full time on site position offers great opportunities for career growth.

Develop and maintain the enterprise AI architecture in alignment with business strategy, enterprise architecture, data strategy, and digital transformation objectives. Assess the current AI and analytics landscape, including models, platforms, data sources, tools, use cases, integrations, and operational capabilities. Define target-state AI architecture and transition roadmaps for enterprise AI adoption. Develop AI architecture principles, standards, patterns, reference architectures, and governance guardrails.

Design architectures for machine learning, deep learning, generative AI, large language models, intelligent automation, and AI-enabled applications. Define enterprise AI platform architecture covering development, training, deployment, inference, monitoring, and lifecycle management. Design GenAI architectures including LLM integration, retrieval-augmented generation, vector databases, prompt orchestration, guardrails, and knowledge integration. Define AI data architecture requirements including data preparation, feature engineering, feature stores, model training data, metadata, and lineage.

Define MLOps and LLMOps architecture for model versioning, deployment, monitoring, retraining, evaluation, and governance. Design secure integration patterns between AI services, enterprise applications, APIs, data platforms, and external AI services. Embed responsible AI, privacy, security, explainability, transparency, fairness, and human oversight requirements into architecture designs. Define architecture requirements for model monitoring, performance, drift, observability, auditability, and operational resilience. Evaluate AI platforms, foundation models, cloud AI services, open-source technologies, and vendor solutions.

Collaborate with enterprise, data, application, integration, cloud, security, and solution architects to ensure coherent end-to-end architectures. Support AI use-case assessment by evaluating feasibility, architecture complexity, data readiness, risk, scalability, and integration requirements. Participate in architecture governance, AI governance, technical design reviews, and Architecture Review Boards. Identify AI architecture risks, dependencies, constraints, ethical considerations, and mitigation actions.

Minimum 8-10 years of experience in technology, data, software engineering, analytics, machine learning, solution architecture, or related disciplines. At least 3-5 years of hands-on experience in AI/ML architecture, AI engineering, data science platforms, or a comparable senior role. Demonstrated experience designing enterprise-scale AI, machine learning, or generative AI solutions. Experience with AI platforms, cloud AI services, model deployment, data pipelines, APIs, and enterprise integration.

Experience with generative AI and large language model architectures is highly desirable. Experience supporting enterprise transformation, innovation, analytics, or AI adoption programs. Experience working with senior stakeholders, data teams, engineering teams, cybersecurity teams, vendors, and governance functions.

Enterprise AI Architecture Machine Learning Architecture Generative AI and LLM Architecture AI platform architecture MLOps and LLMOps Retrieval-Augmented Generation (RAG) Vector database and semantic search architecture AI data pipelines and feature engineering architecture AI integration and API architecture Responsible AI and AI governance AI security and privacy Model monitoring and observability Cloud AI architecture Architecture governance and assurance

TOGAF Archi Mate Cloud architecture and AI well-architected principles Responsible AI principles and governance practices MLOps and model lifecycle management practices Data governance and metadata management practices API-first and event-driven integration patterns Zero Trust and secure-by-design principles for AI environments

AI and machine learning services across Microsoft Azure, AWS, Google Cloud, or equivalent platforms. Generative AI and foundation model platforms, including enterprise LLM services and model orchestration technologies. Machine learning frameworks such as PyTorch, Tensor Flow, scikit-learn, or equivalent. MLOps platforms and model lifecycle management tools. Vector…