Platform Engineer

Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we’ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here.

We don’t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.

Our business, and reputation, is growing fast and we’re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.

AI is an epoch-defining technology, join a company where you’ll be empowered to envision its most powerful applications, and to make them happen.

About the role

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.

What you'll be doing:

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 Dev Sec Ops  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.

Who we're looking for:

You are an engineer who is passionate about building internal tools and takes pride in creating the foundational systems that enable others to excel.

You being experience and knowledge of at least one of the major cloud providers (AWS, GCP or Azure) 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 Docker containerisation and orchestration, to manage distributed systems  have a strong background in Infrastructure-as-Code (IaaC) using tools like Terraform or Cloud Formation, combined with a deep interest in Dev Sec Ops  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.

Our Interview Process Talent Team Screen (30 minutes)
Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes)
Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know…