Software Engineer - Platform

Requirements

• You are a Software Engineer who is passionate about building internal tools and takes pride in creating the foundational systems that enable others to excel
• You understand the nuances of the machine learning product lifecycle and have a clear vision for how to move models efficiently from exploration to production
• You possess modern systems programming skills in Python or Go, and you are comfortable selecting the best-fit technology for complex infrastructure challenges
• You bring practical experience with containerisation and orchestration, specifically using Docker and Kubernetes to manage distributed systems at scale
• You have a strong background in Infrastructure-as-Code (IaaC) using tools like Terraform or CloudFormation, combined with a deep interest in DevSecOps practices
• You thrive in small, ambitious teams where you can take high levels of ownership and communicate effectively with both technical and non-technical peers
• If you don’t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles

What the job involves

• As a Software Engineer in the AI Platform team, you will be the architect of the infrastructure that makes world‑class AI possible. Working closely with the Applied AI team, you’ll be building and maintaining the data science, MLOps, and deployment tooling that empowers our team of over 100 Data Scientists and Engineers
• You will take ownership of the platform that enables us to transition from complex exploration to full‑stack, production‑grade machine learning products, ensuring our solutions are high‑performing, scalable, and seamlessly integrated into diverse client environments
• Taking ownership of our existing deployment and MLOps tooling to ensure our software delivery remains a significant lever for quality and reliability
• Contributing to the continuous evolution of our technology stack, from building new features in our notebook development environments to refining model monitoring systems
• Collaborating with a small, fast‑moving team of customer‑facing technologists to design and build the infrastructure our delivery teams need to succeed
• Designing and implementing infrastructure‑as‑code and DevSecOps processes to support distributed, containerised microservices architectures
• Integrating our core platform services across multiple cloud environments, including AWS, Azure, and GCP, to provide flexible solutions for our global clients
• Scaling our internal enablement capabilities, acting as an entrepreneurial force that removes technical friction and accelerates the deployment of machine learning
• We are open to conversations about part‑time hours

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