Platform Engineer

Faculty is looking for a Platform Engineer to join its Applied AI Platform Team. The successful candidate will provide the internal delivery platform that makes world-class AI possible.

As an engineer in the Applied AI Platform Team, you will provide the internal delivery platform that makes world-class AI possible. You will take ownership of a collection of Cloud infrastructure, tools, templates, and processes that technical teams use to launch and accelerate projects.

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 Machine Learning Engineers. You will encourage engineering best practices, research new technologies, design and build new infrastructure and promote ways of working that enables us to transition from complex exploration to full-stack, production-grade machine learning products.

Responsibilities:

• Taking ownership of existing deployment and MLOps tooling to ensure software delivery remains a significant lever for quality and reliability.
• Contributing to the continuous evolution of the technology stack, from building new features in 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 delivery teams need to succeed.
• Designing and implementing infrastructure-as-code and DevSecOps processes to support distributed, containerised microservices architectures.
• Integrating core platform services across multiple cloud environments, including AWS, Azure, and GCP, to provide flexible solutions for global clients.
• Scaling internal enablement capabilities, acting as an entrepreneurial force that removes technical friction and accelerates the deployment of machine learning.

Requirements:

• Experience and knowledge of at least one of the major cloud providers (AWS, GCP or Azure).
• Modern systems programming skills in Python or Go.
• Practical experience with Docker containerisation and orchestration.
• Strong background in Infrastructure-as-Code (IaaC) using tools like Terraform or CloudFormation.
• Deep interest in DevSecOps practices.

Benefits:

• Opportunity to work on cutting-edge AI projects.
• Collaborative and dynamic work environment.
• Chance to develop and apply technical skills.

Interview Process:

1. Talent Team Screen (30 minutes)
2. Pair Programming Interview (90 minutes)
3. System Design Interview (90 minutes)
4. Commercial Interview (60 minutes)