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.