Production ML Engineer: Real-World AI Solutions

Faculty; Greater London; Hybrid; GBP 70,000 - 100,000; Full time

Benefits offered by this job

Unlimited Annual Leave Policy

Private healthcare and dental

Enhanced parental leave

Family-Friendly Flexibility & Flexible

Sanctus Coaching

Hybrid Working

Job summary

Faculty is seeking a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients. You will bring machine learning from lab to real-world production, contributing to scalable software architecture and best practices.

You’ll work with clients and cross‑functional teams to ensure feasible, high‑quality ML systems are delivered on time. You’ll operate across the ML lifecycle, collaborating with data scientists and engineers, and using frameworks like TensorFlow/PyTorch.

Qualifications

• Understanding the full ML lifecycle and deploying models to production.
• Strong Python development and software engineering practices.
• Experience with cloud platforms and infrastructure security.

Responsibilities

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

Skills

Python

ML Lifecycle

Cloud platforms

Communication

ML frameworks

Education

Bachelor's degree in Computer Science or related

Tools

Docker

Kubernetes

AWS

Azure

GCP

Job description

Faculty is seeking a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients. You will bring machine learning from lab to real-world production, contributing to scalable software architecture and best practices.

You’ll work with clients and cross‑functional teams to ensure feasible, high‑quality ML systems are delivered on time. You’ll operate across the ML lifecycle, collaborating with data scientists and engineers, and using frameworks like TensorFlow/PyTorch.