Platform Engineer

Faculty is hiring a Platform Engineer for a remote role open to applicants in United Kingdom. Level rates it ; you can apply on Faculty.

Level's read on this job

On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
The posting lists this role as remote.
Tools and skills the posting cites include MLOps and AI safety.
Level lists 83 other open roles at Faculty.

AI in this role

ml-opsai-safety

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 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.

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 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.

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 that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We’re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.

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. We are open to conversations about part-time hours.

A note on AI: we're happy for you to use it for research and interview prep, but please don't use it to generate answers during live interviews. We also use an AI note-taker (Metaview) in interviews so interviewers can stay present (which you can opt out of just let us know,) and every application is reviewed by a human, never decided by AI.

How we rate this

Platform Engineer at Faculty rates 61 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Platform Engineer is an open job at Faculty.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in London and is marked remote.
The job was posted on March 9, 2026.
Applications are handled by Faculty. Apply on Faculty

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI Safety

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How do you think about the risk of an AI system in this kind of role failing silently?
Describe a typical day in a role like this one: which parts run through AI directly?
If you removed AI from this role, what would be left, and how do you decide what still needs a human?

Adapt your resume

List these exact terms on your resume: ML Ops and AI Safety. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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