Software Engineer - AI Platform

About Faculty

Faculty transforms organisational performance through safe, impactful and human-led AI. 

We are Europe’s leading applied AI company, and saw its potential a decade ago - long before the current hype cycle. 

We founded in 2014 with our Fellowship programme, training academics to become commercial data scientists. 

Today, we provide over 300 global customers with industry-leading software, and bespoke AI consultancy for retail, healthcare, energy, and governmental organisations, as well as our award winning Fellowship. 

Our expertise and safety credentials are such that OpenAI asked us to be their first technical partner, helping customers deploy cutting-edge generative AI safely. 

Our high-impact work has saved lives through forecasting NHS demand during covid, produced green energy by routing boats towards the wind, slashed marketing spend by predicting customer spending habits, and kept children safe online. 

AI is an epoch-defining technology. We want people to join us who can help our customers reap its enormous benefits safely.

About the role 

Faculty Applied AI is the professional services arm of Faculty. As a founding member of the Applied AI platform team you will be responsible for building and maintaining the data science, ML ops, and deployment tooling that enables our team of over 100 Data Scientists and Engineers to deploy full-stack machine learning products for our customers. 

What you’ll be doing 

You’ll work closely, as part of a small team, in collaboration with our customer-facing technologists. The platform you own will be a significant lever on the quality of our deployed software. Taking ownership for our existing tooling, you’ll identify and prioritise the technology our teams need to deliver our world-leading machine learning solutions; whether that’s new features for our notebook development environment, improvements to our model monitoring tooling or how we deploy our systems into a variety of client environments. 

What we’re looking for

• A software engineer who loves building tools to enable others, understanding their impact, and improving them.
• Understanding of the machine learning product lifecycle and how data scientists go from exploration to deployed models.
• Systems programming in at least one programming language
• Infrastructure-as-code and DevSecOps in one or more major cloud environment
• Experience working as part of a small, fast-moving team
• Someone who takes ownership of what they build and supports what they deploy 

Specifics that will help:

• MLOps using MLFlow.
• Programming in Scala and Python: Some of our existing tooling is in scala, and python is the language of our data science workflows, but we’re open to using the right technologies for the job
• DevSecOps: Gitlab CI/CD, Helm
• IaaC: Cloudformation, Terraform 
• Distributed, containerised microservices architectures: docker, kubernetes
• Experience in a platform/internal enablement team