Artificial Intelligence Lead

Job Description

Design, build, test and maintain software solutions while contributing AI-enabled implementation capabilities that improve delivery speed, quality and operational reliability. The role supports modern engineering practices through software development, automated testing, CI/CD enablement, workflow automation, API integration, AI-assisted implementation patterns and repeatable engineering assets, while operating within agreed engineering standards, architectural guardrails and documentation requirements.

The role will provide executive leadership across AI strategy, innovation, delivery, architecture, engineering standards, governance, data enablement, adoption, benefits realisation and capability development

Qualifications

Type of Qualification: First Degree

• Field of Study: Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, Mathematics, Information Technology or a related field.
• Master's degree in Artificial Intelligence, Data Science, Computer Science, Business Administration, Analytics or a related discipline.
• Professional certifications in Artificial Intelligence, Machine Learning, Cloud Platforms, Data Science, Responsible AI or Enterprise Architecture will be an added advantage

Experience Required

8-10 years

• Proven experience in modern engineering practices (e.g., DevOps, Agile) across multiple engineering disciplines, with depth in at least one area.
• Proven track record delivering large-scale AI and Machine Learning programs from strategy and design through production deployment, adoption and measurable business impact.
• Demonstrated knowledge of AI governance, model risk, cybersecurity, data governance, cloud AI platforms and emerging AI regulation.
• Experience in banking, financial services, fintech, telecommunications, consulting or another highly regulated industry.
• Broad experience in translating business and functional requirements into technical specifications and developing the programming code to create the solutions.

Behavioural Competencies

• Articulating Information
• Developing Expertise
• Documenting Facts
• Embracing Change
• Examining Information
• Interpreting Data
• Producing Output
• Taking Action
• Team Working

Technical Competencies

• Generative AI, Agentic AI and Machine Learning
• AI architecture, MLOps and LLMOps
• Responsible AI and model risk management
• Cloud AI platforms and reusable AI services
• Data governance, security and regulatory compliance
• Production monitoring and cost optimisation