Description
We are seeking a Senior AI Software Engineer to design, develop, and implement intelligent software solutions that drive innovation, improve engineering productivity, and deliver measurable business value.
You will play a key role in building scalable, secure, and production-ready AI systems and applications, working closely with AI Architects, Data Engineers, Data Scientists, Product teams, and Engineering colleagues.
Your responsibilities will include:
- Developing and delivering high-quality software solutions using industry-aligned programming languages, frameworks, and tools
- Collaborating with cross-functional teams to define software requirements, devise solution strategies, and ensure seamless integration and alignment with business objectives
- Participating in code reviews and promoting a culture of code quality and knowledge sharing
- Staying informed of industry technology trends and innovations and contributing to the organisation's technology communities
- Adhering to secure coding practices to mitigate vulnerabilities and protect sensitive data
As a Senior AI Software Engineer, you should possess:
- Strong knowledge of Generative AI, AI-assisted code generation, automated testing, code review acceleration, agentic workflows, and development process optimisation
- Programming and Software Engineering skills – Develop, integrate, and maintain AI-enabled software solutions using languages such as Python, Java, R, and C++
- Machine Learning and AI Fundamentals – Solid knowledge of supervised and unsupervised learning, neural networks, model evaluation, prompt engineering, code transformation and refactoring, data transformation and schema conversion, automated test case generation, and responsible AI practices
- Data Handling and Analysis experience – Ability to work with large and complex datasets
- AI Engineering and Product Delivery – Experience building AI-powered capabilities into production applications
Additional skills include:
- Automation and Tooling Management – Expertise in tooling selection, developer productivity tooling, Infrastructure as Code tools, CI/CD implementation, automated quality controls, and engineering workflow optimisation
- Cloud and Deployment Knowledge – Familiarity with deploying AI models and AI-enabled services using cloud platforms such as AWS, Azure, or GCP
- Problem-Solving and Communication skills – Ability to translate complex business and engineering challenges into practical AI solutions