Production ML Engineer - Real-World AI Systems

Faculty Science Limited; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Unlimited Annual Leave

Private healthcare

Enhanced parental leave

Hybrid Working

Wellbeing support

Job summary

Faculty Science Limited is hiring a Machine Learning Engineer to bring production-grade ML into real-world client solutions. You will help shape scalable software architecture, define best practices for deployment and work with cross-functional teams to ensure timely delivery of high-quality ML systems.

You will operate across the full ML lifecycle, applying frameworks like TensorFlow or PyTorch, and leverage cloud platforms and container orchestration to deploy at scale.

Qualifications

• Experience with end-to-end ML project lifecycle and productionising models.
• Strong Python coding skills and software engineering practices.
• Hands-on with cloud platforms and container orchestration for scalable ML applications.
• Ability to translate complex ML concepts for non-technical stakeholders.

Responsibilities

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable ML solutions to accelerate delivery.
• Collaborate with engineers, data scientists and commercial leads on client challenges.
• Lead technical scoping and architectural decisions for feasibility and impact.
• Define and implement standards for deploying ML at scale.

Skills

Python

ML deployment

Cloud platforms

Software engineering best practices

Communication

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

AWS

Job description

Faculty Science Limited is hiring a Machine Learning Engineer to bring production-grade ML into real-world client solutions. You will help shape scalable software architecture, define best practices for deployment and work with cross-functional teams to ensure timely delivery of high-quality ML systems.

You will operate across the full ML lifecycle, applying frameworks like TensorFlow or PyTorch, and leverage cloud platforms and container orchestration to deploy at scale.