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

Apply Now

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Owen Thomas | Pending B Corp
City of London
1 day ago
Create job alert

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000


About the Company

Our client is an extremely well know, digital marketplace focused on sustainable ecommerce. With over 35 million of active users globally, they’re redefining how people buy and sell second-hand fashion, aiming to make the future of style both circular and accessible.


The company has offices in UK, EU and US and experienced significant growth especially around the US market and now operates as part of a leading global e-commerce group. They pride themselves on fostering inclusivity, creativity, and innovation and values that extend to both their community and their teams.


The organisation champions diversity, equal opportunity, and flexible working. They offer a progressive benefits package designed to support wellbeing, learning, and work-life balance.


The role of Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000


Our Client is looking for an experienced MLOps Engineering Manager to lead and scale their MLOps function. You will be shaping how machine learning is developed, deployed, and scaled across the organisation.


This is a genuinely high-impact role: you’ll lead a talented team of 6-8 engineers, set the strategic direction for ML infrastructure, and ensure the business continues to deliver reliable, scalable, and high-performing ML systems that drive real-world impact.


Key Responsibilities for Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | London, Hybrid, 1 Day PW, Up to £140,000

  • Manage and develop a team of 8 MLOps engineers, fostering collaboration, high performance, and personal growth.
  • Define and deliver the MLOps roadmap, aligning closely with the wider engineering and data strategy.
  • Provide guidance on architecture, tooling, and best practices for ML pipelines, deployment, monitoring, and incident management.
  • Partner with data science, ML, and product teams to ensure infrastructure supports innovation and business needs.
  • Oversee system reliability, cost optimisation, and vendor relationships to keep infrastructure scalable and efficient.
  • Take ownership of critical ML/infra incidents, ensuring swift resolution and continuous learning.
  • Deliver clear progress, risk, and priority updates to leadership in a concise and actionable way.


Requirements for the role:

  • Proven experience leading an MLOps, ML Engineering, or Platform Engineering team.
  • Solid background in applied machine learning and a passion for platform disciplines.
  • Hands-on experience with cloud platforms (AWS, GCP, or Azure), including large-scale ML infrastructure management.
  • Knowledge of GPU computing for model training and serving.
  • Experience managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI).
  • Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed).
  • Strong understanding of monitoring, logging, and observability for large-scale ML systems.
  • Experience in cost optimisation for compute/GPU workloads.
  • Excellent people leadership and communication skills, able to influence technical and non-technical stakeholders.
  • Comfortable working in a fast-paced, collaborative environment with strategic and operational responsibilities.
  • Experience with vendor management and contract oversight.
  • Familiarity with tools such as Databricks, Tecton (or Feast), Seldon, or SageMaker.


What can they offer you?

  • Private health and mental wellbeing coverage, including access to counselling and coaching.
  • Salary of up to £140,000+Bonus & Benefits
  • 25 days annual leave, plus additional company-wide rest days and volunteer leave.
  • Flexible hybrid working, with the option to work abroad for limited periods.
  • Generous parental, IVF, and carer leave policies.
  • Learning and development budgets for conferences, mentorship, and skills growth.
  • Pension matching, life insurance, and recognition for service milestones.


If you are interested in the Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000 then drop over your CV and we will give you a call if we think you are a good fit!

Related Jobs

View all jobs

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

Engineering Manager, MLOps, Marketplace, Ecommerce, | 35 Million Users | UK Remote OR London, Hybrid, 1 Day PW, Up to £140,000

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.