Software Engineer, VP

Join us as a Software Engineer

• This is an opportunity for a technically minded individual to join us as a Software Engineer
• You’ll be designing, producing, testing and implementing working software, working across the lifecycle of the system
• Hone your existing software engineering skills and advance your career in this critical role
• We're offering this role at vice president level What you'll do Working in a permanent feature team, you’ll be developing knowledge of aspects of the associated platform across the disciplines of business, applications, data and infrastructure. You’ll also be liaising with principal engineers, architects in the domain and other key stakeholders to understand how the platform works and how it supports business objectives.

You’ll Also Be

• Applying Agile methods to the development of software on the backlog
• Producing resilient and long-lived software and acting flexibly to cope with future needs
• Delivering intentional architecture and formulating emergent design through innovative ideas, experimentation and prototyping
• Designing and developing software with a focus on the automation of build, test and deployment activities, using executable patterns The skills you'll need We’re looking for someone with strong full stack experience in software design and implementation, including being able to exploit programming languages to solve complex problems. You’ll also need to be capable of complex requirements analysis capture and validation against and with business and systems requirements.

Additionally, You’ll Demonstrate

• Build and operate scalable AI/ML & GenAI platforms - Deliver cloud‑native AI/ML infrastructure using AWS, Terraform, Python, and containerized microservices, hands‑on experience with GCP and Azure is a plus.
• Deliver end‑to‑end AI/ML solutions - Lead the full lifecycle, from solution design and architecture to deployment, monitoring, optimisation, and operational support.
• Enable advanced model operations - Implement model serving, LLMOps practices, governance, observability, drift/performance monitoring, and high‑availability model‑serving patterns.
• Drive automation and engineering excellence - Develop CI/CD pipelines for ML workloads, strengthen platform security/compliance, optimize GPU usage, and embed cloud‑native engineering best practices.
• Collaborate across technical teams - Work closely with engineering, datascience, and architecture teams to deliver secure, reliable, and production‑ready AI solutions with consistent architecture alignment.