Production ML Engineer - Scalable AI Solutions

Faculty; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Job summary

Faculty is seeking a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients in the Energy, Transition and Environment space. You will bring ML out of the lab into production, shaping scalable software architecture and best practices while working with clients and cross-functional teams to ensure feasible, timely delivery of high-quality ML systems.

You’ll lead technical scoping, advise on deployment at scale, and translate complex ML concepts for stakeholders, contributing

Qualifications

• Experience operationalising models with Scikit-learn, TensorFlow or PyTorch.
• Strong Python and software engineering practices.
• Experience with cloud platforms and security.
• Hands-on with containers and orchestration (Docker & Kubernetes).
• Excellent communicator able to guide technical and non-technical stakeholders.

Responsibilities

• Building and deploying production-grade ML software, tools, and infrastructure.
• Creating reusable, scalable solutions for ML systems.
• Collaborating with engineers, data scientists, and commercial leads on client challenges.
• Leading technical scoping and architectural decisions for feasibility and impact.
• Defining and implementing Faculty’s standards for deploying ML at scale.
• Acting as a technical advisor to customers and partners, translating ML concepts for stakeholders.

Skills

ML lifecycle

Python proficiency

Communication

Team collaboration

Autonomy

Tools

Scikit-learn

TensorFlow

PyTorch

Docker

Kubernetes

AWS

Azure

GCP

Job description

Faculty is seeking a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients in the Energy, Transition and Environment space. You will bring ML out of the lab into production, shaping scalable software architecture and best practices while working with clients and cross-functional teams to ensure feasible, timely delivery of high-quality ML systems.

You’ll lead technical scoping, advise on deployment at scale, and translate complex ML concepts for stakeholders, contributing