AI Director

Description Job Purpose: To lead the development, implementation, and scaling of artificial intelligence solutions across the Group bytranslating clinical, operational, and business needs into measurable outcomes. The role is responsible for establishing and leading the AI Center of Excellence, executing the Group’s AI strategy and governance framework, developing internal AI capabilities, and collaborating with key stakeholders to identify, design, validate, deploy, monitor, and scale high-value AI solutions. The Director will also provide technical leadership in solution architecture, evaluation, prototyping, implementation decisions, and the resolution of technical and operational challenges, while identifying and supporting innovation, research, and commercialization

Key Responsibilities And Duties

• AI Strategy Execution and Portfolio Management:
• Support the execution of the Group’s enterprise AI strategy and translate strategic priorities into an actionable AI roadmap and portfolio of initiatives.
• Develop and maintain a prioritized portfolio of AI initiatives with defined objectives, business owners, technical requirements, timelines, expected outcomes, and measures of success.
• Establish a structured process for identifying, evaluating, and prioritizing AI opportunities based on clinical or operational value, technical feasibility, data readiness, implementation requirements, risk, scalability, and expected return on investment.
• Maintain a balanced portfolio across clinical AI, predictive analytics, operational optimization, administrative automation, workforce productivity, patient engagement, natural language processing, generative AI, and other emerging AI capabilities.
• Provide regular visibility into the AI portfolio, including implementation progress, dependencies, risks, resource requirements, outcomes, and opportunities for scale.
• AI Solution Development and Implementation:
• Lead AI initiatives through the complete delivery lifecycle from problem identification and feasibility assessment through prototype, minimum viable product, validation, integration, production deployment, adoption, monitoring, and scale.
• Work directly with software developers, data engineers, clinical informaticists, clinicians, and business teams to translate organizational needs into deployable AI solutions.
• Develop, prototype, or technically lead the development of AI and machine learning solutions where appropriate.
• Establish rapid prototyping capabilities to evaluate promising ideas before significant resources are committed.
• Ensure successful prototypes and pilots have clearly defined pathways to production and enterprise adoption.
• Maintain accountability for delivery timelines, technical quality, implementation readiness, adoption, and measurable outcomes.
• AI Technology and Engineering:
• Provide technical leadership across modern AI technologies including machine learning, deep learning, predictive analytics, natural language processing, large language models, generative AI, AI agents, and intelligent automation.
• Evaluate model architecture, APIs, datasets, AI frameworks, technical documentation, model performance, infrastructure requirements, and technical claims.
• Guide technical decisions related to model selection, fine tuning, retrieval augmented generation architectures, agentic workflows, data pipelines, inference architecture, integration, and deployment.
• Participate directly in architecture reviews, technical problem solving, prototype evaluation, and resolution of implementation barriers.
• Stay current with developments in AI and assess emerging technologies based on their potential to address meaningful clinical, operational, and business needs
• MLOps, LLMOps and Production AI
• Establish practical MLOps and LLMOps capabilities to support reliable development, deployment, and operation of AI solutions.
• Implement processes for model and prompt versioning, testing, deployment pipelines, monitoring, observability, performance evaluation, drift detection, retraining, rollback, and lifecycle management.
• Ensure production AI solutions meet enterprise requirements for scalability, reliability, availability,performance, cybersecurity, privacy, supportability, disaster recovery, and business continuity.
• Establish ongoing monitoring of AI performance, safety, reliability, utilization, and outcomes following deployment

5- Healthcare AI And Clinical Integration

• Work closely with physicians, nurses, pharmacists, allied health professionals, clinical informatics, quality, and operational teams to identify opportunities where AI can improve clinical care, quality, safety, productivity, and patient experience.
• Translate clinical needs and workflows into technical requirements and ensure solutions are designed around actual clinical and operational processes.
• Lead the technical and operational integration of AI solutions with electronic medical records, clinical systems, enterprise applications, data platforms, APIs, and other healthcare technology environments in coordination with the relevant enterprise technology teams.
• Apply healthcare interoperability standards and technologies including HL7, FHIR, APIs, clinical terminology standards, and healthcare data models where applicable.
• Establish appropriate retrospective and prospective validation methodologies for clinical AI solutions in collaboration with clinical, quality, and research stakeholders.
• Ensure clinical AI solutions undergo appropriate safety, performance, usability, workflow, and clinical acceptance assessments prior to production implementation.
• Work with clinical teams throughout implementation to support adoption, evaluate unintended consequences, and refine solutions based on real world performance and user feedback.

6- AI Governance and Responsible AI

• Further develop and operationalize the Group’s AI governance framework in alignment with established enterprise governance, cybersecurity, privacy, legal, clinical, and regulatory requirements.
• Coordinate governance across the AI lifecycle including ideation, technical assessment, data readiness, cybersecurity and privacy review, regulatory review, validation, clinical acceptance, deployment readiness, monitoring, auditing, incident management, and post deployment oversight.
• Embed responsible AI principles throughout solution development and deployment including data quality, privacy, security, bias, fairness, transparency, explainability, human oversight, and appropriate use.
• Work closely with cybersecurity, privacy, legal, quality, clinical governance, and other relevant stakeholders to identify and manage risks throughout the AI lifecycle.
• Ensure governance requirements are incorporated into AI development and implementation processes from the outset, enabling safe and efficient progression of approved solutions into production.
• Data and AI Readiness
• Work with data engineering, analytics, clinical informatics, and technology teams to ensure data required for AI initiatives is accessible, reliable, appropriately governed, and fit for purpose.
• Assess data availability, quality, completeness, and suitability early in the AI development lifecycle.
• Define requirements for data extraction, normalization, labeling, terminology mapping, training and validation datasets, data pipelines, lineage, and access controls.
• Identify and address data limitations that may affect model performance, clinical validity, scalability, or implementation feasibility.

Requirements Additional Key Responsibilities and Requirements:

• AI Technology Evaluation and Technical Due Diligence:
• Conduct technical and operational assessments of proposed AI solutions and technologies, including architecture, model performance, validation evidence, data requirements, integration capabilities, scalability, cybersecurity considerations, privacy requirements, supportability, and technical risks.
• Provide evidence based technical recommendations on the suitability, readiness, and implementation requirements of AI solutions for consideration through the Group’s established technology, procurement, governance, and investment processes.
• Lead or support proof of concept and pilot evaluations where appropriate to determine technical feasibility, clinical applicability, integration requirements, performance, and expected value.
• Work with clinical, technology, data, cybersecurity, privacy, procurement, legal, and business stakeholders to define technical requirements and ensure that proposed AI solutions can be implemented safely and effectively within the Group’s enterprise environment.
• Identify opportunities where internal AI development or co development may be technically feasible and provide recommendations through the appropriate organizational governance and approval channels.
• Value Realization and Adoption
• Define measurable success criteria and expected benefits for AI initiatives before development begins.
• Establish baseline measurements and evaluate post implementation outcomes including clinical impact, productivity, operational efficiency, quality and safety, financial performance, workforce impact, and
• patient experience.
• Monitor adoption and utilization of deployed AI solutions and work with clinical and business owners to identify and address barriers to adoption.
• Scale successful solutions across appropriate facilities, departments, and workflows following the relevant organizational approvals.
• Recommend modification, redesign, or discontinuation of initiatives that fail to demonstrate sufficient value, adoption, technical performance, or clinical benefit.
• Provide regular reporting on AI portfolio performance, implementation progress, adoption, benefits realization, risks, and lessons learned.
• AI Center of Excellence Development
• Establish and progressively develop the Group’s AI Center of Excellence.
• Define the technical capabilities, roles, development standards, methodologies, tools, infrastructure requirements, and operating model required to support enterprise AI delivery.
• Build and lead a multidisciplinary team incorporating capabilities such as AI and machine learning engineering, data science, data engineering, AI product management, MLOps and LLMOps, clinical
• informatics, validation, and program management.
• Recruit, mentor, and develop AI and data professionals while strengthening AI capabilities across the organization.
• Develop reusable AI components, reference architecture, development standards, documentation, and implementation practices to accelerate future projects and enable solutions to scale.
• Promote collaboration and knowledge sharing across clinical, technology, research, data, and business teams.
• Innovation, Research and Commercialization Support:
• Identify and evaluate emerging AI technologies, startups, academic collaborations, research opportunities, and potential innovation partners relevant to the Group’s strategic priorities.
• Support the development and technical evaluation of collaborations with universities, startups, technology companies, small and medium sized enterprises, research organizations, and other innovation partners.
• Support applied AI research and clinical validation projects aligned with organizational priorities.
• Contribute to postgraduate research and student supervision where appropriate.
• Provide technical input into potential co development opportunities, including feasibility, required capabilities, data requirements, intellectual property considerations, implementation requirements, and potential scalability.
• Identify potential intellectual property arising from internally or jointly developed AI solutions and work with the appropriate organizational stakeholders to ensure that datasets, algorithms, models, software, and other intellectual assets are appropriately recogn