Principal Software Engineer

We are seeking a Principal Software Engineer and AI thought leader to help define and drive the AI `strategy across the Integrted engineering organisation. This role will provide technical vision, strategic direction, and practical leadership to identify and deliver AI-enabled capabilities that improve customer experiences, accelerate engineering productivity, strengthen operational resilience, and create a competitive advantage.
As a Principal Software Engineer, you will combine deep software engineering expertise with AI leadership to guide how teams adopt, evaluate, and scale AI across the software development lifecycle. You will promote responsible AI practices, modern engineering techniques, automation, Continuous Delivery, and measurable continuous improvement.
You will be recognised as a trusted technical authority, strategic advisor, mentor, and change agent who helps engineering teams adopt AI in a pragmatic, secure, and value-driven way.
What You’ll Be Doing
Help to define and lead the AI strategy for the Integrated engineering organisation, aligned to business priorities, product direction, architecture principles, security expectations, and measurable outcomes.
Accelerate organizational AI maturity by fostering collaboration, growing internal AI communities, and expanding awareness of AI capabilities that empower teams to innovate and deliver greater impact with engineering and peers within Operations, DevOps, Security and Compliance teams
Identify, prioritise, and validate high-impact AI opportunities across product experiences, engineering workflows, developer productivity, quality engineering, operations, support, platform modernisation, knowledge management, and partner ecosystems.
Improve engineering effectiveness through applied AI, automation, simplification, modern tooling, and continuous improvement.
Champion responsible and secure AI adoption, ensuring solutions account for data protection, privacy, reliability, explainability, observability, operational support, regulatory expectations, and ethical use.
Lead AI engineering enablement by establishing standards, reusable patterns, reference architectures, evaluation approaches, knowledge-sharing forums, and mentoring practices.
Coach and mentor engineers to adopt AI-first thinking where it improves outcomes, while maintaining strong engineering discipline, quality, and customer focus.
Embed quality throughout the software lifecycle by promoting observable, testable, reliable, and supportable AI-enabled engineering outcomes.
What We Ask For
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field, combined with a passion for developing talent and building high-performing teams.
Strong hands-on engineering background with experience building, deploying, and scaling production-ready software applications and AI-powered products.
Exceptional communication, stakeholder management, and storytelling skills with the ability to influence decisions across technical and non-technical audiences.
Experience implementing AI/ML operational practices, including continuous integration/continuous deployment (CI/CD), automation, data governance, or real-time data processing solutions.
Understanding of responsible AI practices, including governance, security, privacy, risk assessment, testing, monitoring, human oversight, auditability, and safe adoption within enterprise engineering environments.
Hands-on experience identifying and resolving performance and scalability issues across distributed application architectures and databases.
Understanding of database design, database development, design patterns, refactoring, object-oriented design, and modern software development best practice.
Experience with web services, including SOAP and RESTful endpoint creation and authentication schemes.
Experience working in Agile Scrum or Lean Kanban environments, with strong analytical, problem-solving, and organisational skills.
What We’d Love You To Have
Payments domain expertise
Experience with Natural Language Processing (NLP), conversational AI agents, or large language model application development frameworks.
Knowledge of cloud and data platforms