Title:  Engineer, AI

Job Description:

• Collaborate closely with multidisciplinary teams—including engineering, operations, and quality assurance—to gather requirements and translate them into viable and user-friendly digital tools.
• Assist in gathering, validating, and analysing data critical for pilot projects, ensuring accuracy and relevance to support performance monitoring and continuous improvement efforts.
• Support end-to-end change management activities, including engaging users, creating comprehensive documentation, and training materials, and providing assistance during the rollout phases to ensure smooth adoption.
• Ensure all digital solutions comply with organisational governance, cyber security policies, and operational standards to maintain integrity and reliability.
• Support and actively contribute to continuous improvement programmes, focusing on digitalisation and automation initiatives aimed at streamlining workflows and enhancing operational efficiency.
• Develop hands-on expertise by working on AI-assisted tools and automation frameworks, assisting in building predictive, data-driven applications that support informed decision-making.

Job Requirements

• Basic understanding of software development principles with practical experience or coursework in at least one programming language or development platform.
• A proactive, self-motivated learner with the capacity to work independently and adapt to evolving priorities within a fast-paced and dynamic environment.
• Strong communication skills paired with a collaborative mindset, enabling effective teamwork and stakeholder engagement.
• Familiarity or experience in full-stack development covering both frontend and backend technologies.
• An interest in or previous exposure to automation technologies, data analytics methodologies, AI-enabled systems, or low-code/no-code development platforms.
• Ability to map and analyses operational processes and workflows, identifying opportunities for automation and system enhancements to eliminate inefficiencies.
• Knowledge of enterprise data platforms, APIs, and systems integration.
• Experience in Data Science and Machine Learning model development. Industry certifications in AI/ML, cloud, or enterprise architecture.