At Ocean Infinity

We're on a bold mission to subsea data using innovative technology.

Using cutting-edge robotics, autonomous technology and world-class software, we're transforming how complex operations are carried out at sea. Our uncrewed systems are making ocean exploration and operations safer, smarter and more sustainable, helping unlock the vast potential of our oceans while reducing environmental impact. 🌍

This isn't a vision for the future. It's happening right now.

As we continue to push the boundaries of innovation and redefine what's possible in the maritime industry, we're looking for exceptional talent to join our fast-growing team.

Ocean Infinity's Data Platform and AI/ML teams are solving increasingly complex challenges, from moving data reliably between vessels and shore environments to deploying AI models and data products at scale.

We're looking for a Head of Platform Architecture to define and lead the shared platform architecture that underpins these capabilities. This role will own the infrastructure, deployment architecture and engineering standards that enable Data, AI/ML and Robotics teams to build, deploy and operate reliably across cloud, on-premises and edge environments.

You will be responsible for creating a coherent platform strategy across a fragmented technology landscape, establishing common standards, improving scalability and reliability, and ensuring teams can deliver quickly without reinventing infrastructure solutions.

What you will do! 🚀

• Design reliable and automated approaches for moving data from vessels to shore-based environments.
• Establish architectures for how data is transmitted, processed, stored and accessed across hybrid cloud and on-premises environments.
• Partner closely with Infrastructure and Data Platform teams to ensure scalable and resilient solutions.
• Define common CI/CD, deployment and infrastructure-as-code standards across Data, AI/ML and Robotics teams.
• Build reusable templates, tooling and patterns that accelerate delivery and reduce duplication.
• Drive adoption of shared platform capabilities across engineering teams.
• Define the platform architecture that enables AI/ML models to move from development into production.
• Establish standards for model packaging, deployment, versioning, monitoring and observability.
• Ensure AI infrastructure can scale efficiently while remaining reliable and maintainable.
• Own platform architecture decisions related to scalability, performance and operational resilience.
• Establish infrastructure governance and reliability standards.
• Improve visibility and control of infrastructure spending across data and AI platforms.
• Drive cost optimisation without compromising performance or delivery speed.
• Develop a target architecture that brings together multiple teams, technologies and environments.
• Define platform roadmaps, architecture principles and engineering standards.
• Maintain architecture documentation and technical decision frameworks.
• Guide the evolution of existing platforms through pragmatic, phased improvements.
• Partner with leaders across Data Platform, AI/ML, Robotics, Infrastructure, Cyber Security and wider business functions.
• Influence technical direction and drive alignment around shared standards.
• Support teams adopting common platform capabilities and best practices.
• Act as the organisation's subject matter expert for platform and infrastructure architecture.