Requirements

At least 8 years of experience in data architecture, including 2–3 or more years designing artificial intelligence and machine learning systems in production.
Strong hands-on experience with cloud data platforms such as Azure, Amazon Web Services, or Google Cloud Platform, and modern data stacks including data lakes, lakehouses, and extract, transform, and load or extract, load, and transform pipelines.
Proven experience designing enterprise data architectures, including data modeling, data warehousing, and master data management.
Solid understanding of the artificial intelligence and machine learning lifecycle, including model selection, machine learning operations, deployment, and monitoring.
Experience with large language model and generative artificial intelligence architecture, including retrieval-augmented generation pipelines, vector databases, prompt engineering, and model orchestration.
Experience with data governance and compliance frameworks.
Ability to translate business requirements into technical architecture and communicate with engineering teams and executive stakeholders.
Experience with application programming interface design and system integration.
Moderate to advanced data engineering and data architecture skills.
Basic to intermediate artificial intelligence engineering skills.
Strong Structured Query Language skills.
At least one programming language, with Python identified as the most common option.

Responsibilities

Design enterprise data architectures, including data modeling, data warehousing, and master data management.
Design artificial intelligence and machine learning systems in production environments.
Develop and manage cloud data platforms and modern data stack architectures, including data lakes, lakehouses, and extract, transform, and load or extract, load, and transform pipelines.
Design and oversee artificial intelligence and machine learning lifecycle activities, including model selection, machine learning operations, deployment, and monitoring.
Architect large language model and generative artificial intelligence solutions, including retrieval-augmented generation pipelines, vector databases, prompt engineering, and model orchestration.
Translate business requirements into technical architecture.
Communicate with engineering teams and executive stakeholders.
Design application programming interfaces and system integrations for interconnected platforms and systems.
Ensure alignment with data governance and compliance frameworks.
Support smart-city, government, and enterprise platform initiatives involving multiple stakeholders and integrated systems.

Desired Qualifications

Direct experience with Personal Data Protection Law, National Cybersecurity Authority Essential Cybersecurity Controls, National Data Management Office frameworks, or similar frameworks.
General Data Protection Regulation or Health Insurance Portability and Accountability Act experience.
Advanced artificial intelligence architecture experience.
Familiarity with real-time or streaming data architectures, including Kafka and event-driven systems.
Experience with Internet of Things data ingestion, such as parking sensors and city infrastructure.
Enterprise architecture certifications, such as The Open Group Architecture Framework or cloud architect certifications.
Experience in government, smart-city, or public-sector platforms.
Experience in vendor or multi-stakeholder environments, including joint ventures, consortia, or large enterprises with multiple business units.
English language skills.
Arabic language skills.

Data & AI Architect

Datamatics Technologies