Machine Learning Engineer

Description

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.

Our Energy, Transition and Environment business unit is pioneering meaningful change in the clean energy revolution. Our vision is to accelerate the transition to net-zero emissions and drive efficiencies for a new era of utility companies.

We believe that the responsible, and intelligent, deployment of AI is critical to the success of this mission. We partner with a wide range of clients - from major energy operators, to GreenTech startups, and national infrastructure providers - to build solutions which return measurable impact and move us towards a smarter, cleaner, and more sustainable world.

About the role

Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients.

You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems.

What you'll be doing:

Building and deploying production-grade ML software, tools, and infrastructure.

Creating reusable, scalable solutions that accelerate the delivery of ML systems.

Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges.

Leading technical scoping and architectural decisions to ensure project feasibility and impact.

Defining and implementing Faculty’s standards for deploying machine learning at scale.

Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders.

Who we're looking for:

You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.

You possess strong Python skills and solid experience in software engineering best practices.

You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.

You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale

You are comfortable with core ML concepts, including probability, statistics, and common learning techniques.

You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders.

You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions