Head of Artificial Intelligence

About the Role

We are facilitating recruitment for a prominent UAE-based energy and utilities organisation driving the region's energy transition. This organisation operates extensive power generation, water, and renewable assets and is heavily investing in digital technologies, with artificial intelligence (AI) and digital twin technologies central to its strategy. Their goal is to enhance asset performance, reliability, and sustainability.

The Head of Artificial Intelligence will be responsible for shaping and championing the AI strategy across a broad asset base. The role is a key leadership position tasked with building and expanding the AI framework, platform, and team to introduce physics-based and data-driven modelling into everyday operations. Use cases include predictive maintenance, process optimization, energy efficiency improvements, and grid intelligence. Collaboration with operations, engineering, and asset management departments is essential.

Key Responsibilities

• Develop the AI roadmap spanning generation, transmission, distribution, and water assets.
• Recruit, lead, and nurture a diverse team consisting of data scientists, simulation engineers, and machine learning engineers.
• Act as the organisation's top expert on industrial AI, advising senior leadership regarding opportunities, investment strategies, and associated risks.
• Deliver impactful AI applications such as predictive maintenance, anomaly detection, energy optimization, demand forecasting, and lifecycle management of assets.
• Implement MLOps methodologies to transition AI models from pilots to reliable, scalable production.
• Ensure AI deployments adhere to critical standards for interpretability, safety, and operational trust, especially within vital infrastructure.
• Oversee the design and application of digital twins at asset, process, and system levels throughout essential infrastructure.
• Combine physics-based simulations, live operational data including SCADA, historians, IoT sensor data, and machine learning within integrated digital twin environments.
• Collaborate with original equipment manufacturers, engineering consultancies, and technology suppliers to expedite solutions while building internal capabilities.
• Design and influence the industrial data framework, including data historians, time-series platforms, cloud and edge computing infrastructures, enabling AI and digital twin workloads.
• Lead initiatives on data governance, quality, and interoperability across operational technology (OT) and information technology (IT) domains.
• Work alongside cybersecurity teams to ensure secure integration between OT and IT systems.
• Develop strong relationships with plant operations, asset management, and engineering teams to ensure on-ground acceptance and adoption of AI technologies.
• Define and measure the financial and operational benefits of AI initiatives including improvements in availability, cost savings, efficiency gains, and emissions reduction.
• Represent the organisation at industry events and government forums related to digital innovations in energy.

Experience and Qualifications

• Over 15 years of experience in industrial digital technology with a minimum of 7 years in leadership roles related to industrial AI, digital twin technology, or advanced analytics programs.
• A proven background within the energy, utilities, oil and gas, or heavy industry sectors, encompassing both technology leadership and operational engagement.
• Demonstrated success in advancing industrial AI and digital twin projects from ideation through to large-scale production.
• Familiarity and experience with prominent industrial platforms such as AVEVA, Bentley, Siemens Xcelerator, GE Vernova, AspenTech, and Cognite, as well as cloud services like Azure and AWS.
• Comprehensive knowledge of physics-based modeling combined with simulation and data-driven methods, understanding when to apply each approach.
• In-depth understanding of industrial data systems including SCADA, PI/OSIsoft historians, IoT, edge computing, and technologies bridging OT and IT infrastructures.
• Proficient in modern machine learning engineering practices and MLOps techniques.
• Awareness of cybersecurity challenges and protocols concerning critical infrastructure environments.
• A degree in Engineering, Computer Science, Physics, or related disciplines is required; advanced degrees are advantageous.