Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Ops Engineer

DigitalGenius
City of London
4 days ago
Create job alert

At DigitalGenius (DG), we are using AI Agents to transform customer experience for ecommerce brands. With a proprietary approach to agentic AI, we have a unique opportunity to become the undisputed leader in our industry. We're looking for excellent candidates to join our dedicated, thoughtful, and hardworking team to help us achieve that goal. We are a global company with offices in London, New York, and people across the world. Our customers include some of the biggest names in retail including On, Rapha, Air Up, Holland & Barrett, AllSaints, Honeylove, and Clarins.


Overview

The continuous improvement of our products and the range of innovation projects we are committed to require us to scale our Machine Learning team. We are looking for an MLOps Engineer to join our core AI Team. This is a highly technical role for an outstanding individual who can take ownership of projects and start new initiatives.


As an MLOps Engineer, you will be responsible for designing, deploying, and maintaining scalable infrastructure and processes that support our AI systems in production. Your time will be divided between improving our ML infrastructure, building deployment and monitoring systems, and working closely with our ML engineers and product teams. This is an excellent opportunity for those with strong Engineering and DevOps capabilities and a deep interest in operationalising AI solutions. We are looking for someone with complementary skills that extend into infrastructure and observability, preferably with experience in E-Commerce.


The AI team owns all ML-related research, implementation and maintenance. In practice, this means keeping up to date with best practices in production ML, developing and supporting scalable infrastructure, and enabling faster and safer experimentation and deployment.


Responsibilities

  • Proactive approach with team members and clients
  • Continuous improvement of ML infrastructure and operations
  • Take ownership of the deployment and monitoring pipelines within your expertise
  • Contribute to the ongoing innovation R&D projects by enabling production readiness
  • Maintain and implement CI/CD pipelines, observability, and infrastructure for ML services

Requirements

  • Degree in relevant field with 3+ years of industry experience
  • Strong Technical Skills: Python, AWS, Docker, Terraform
  • Experience deploying and maintaining machine learning models in production environments
  • Familiarity with MLOps best practices: versioning, monitoring, model registries, CI/CD
  • Excellent organisation skills, working independently and ability to deliver results for deadlines
  • A proactive, innovative, pragmatic approach to problem-solving and an ability to think critically and objectively
  • Good customer-facing skills and ability to communicate technical concepts to technical and non-technical audiences
  • Experience in E-Commerce space

Benefits

  • Competitive Salary
  • Generous vacation time (25 days)
  • Yearly "DG Recharge Week" in addition to annual leave allowance
  • Freedom to experiment with your own ideas
  • Environment to develop your skills without bureaucracy or red tape
  • Monthly fitness stipend of $210 or fully paid Third Space Membership
  • Ongoing subscription to Mental Health Support Platform

We are an equal-opportunity employer and value diversity at our company. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.


#J-18808-Ljbffr

Related Jobs

View all jobs

Staff Machine Learning Platform/Ops Engineer

Machine Learning Operations Lead

Machine Learning Operations Engineer

Machine Learning Engineer

Machine Learning Operations Engineer

Machine Learning Operations Engineer

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.