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.

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

Machine Learning Engineer

Key skills