Description
Faculty is looking for a Machine Learning Engineer to deliver bespoke AI solutions for its Financial Services clients.
The successful candidate 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.
Responsibilities:
- 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.
Requirements:
- Understanding of the full machine learning lifecycle and experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
- Strong Python skills and solid experience in software engineering best practices.
- Hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security.
- Experience with container and orchestration tools such as Docker & Kubernetes to build and manage applications at scale.
- Comfortable with core ML concepts, including probability, statistics, and common learning techniques.
- Excellent communication skills, able to guide technical teams and confidently advise non-technical stakeholders.
- Ability to thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and deliver solutions.