Senior ML Ops Engineer

Aitopics

London

1 year ago

Applications closed

Related Jobs

View all jobs

Hybrid Machine Learning Engineer - Build Impactful Models

Machine Learning Engineer Contractor

Senior Data Engineer-Real-Time ETL, Kafka & ML Ops (Hybrid)

Senior Machine Learning Engineer (MLOps)

AI & Data Science Manager / Senior Manager

Enterprise Architect - Data Science, AI and ML

Role Title:Senior Machine Learning Operations Engineer (MLOps)

Location:London, Farringdon (Hybrid)

Royal Mail delivers more than our competitors put together. Yet we have ambitious plans to grow market share both at home and globally, whilst transforming our UK operation to increase efficiency and profit. Our strategy clearly sets out these plans – data and technology is pivotal to its success.

In this role you’ll play a crucial part in executing the strategic roadmap for data and analytics. Drawing on the latest technical innovations, you will enable data-driven decision-making across Royal Mail to deliver value for our customers, our people, and our shareholders.

You will work with and lead the technical direction of multi-disciplinary project and programme teams to contribute to the development and successful execution of Royal Mail’s data strategy. You will provide technical analytical expertise and mentorship to colleagues to lead usage and implementation of machine learning operations capability, refining data policies and best practices where appropriate. You will ensure that we deliver business value from our data assets.

What will you do?

Design, develop, and implement MLOps pipelines for the continuous deployment and integration of ML models
Collaborate with data scientists to understand model requirements and optimise deployment processes
Automate the training, testing and deployment processes for machine learning models
Monitor and maintain models, ensuring optimal performance, accuracy and reliability
Implement best practices for version control, model reproducibility and governance
Optimise machine learning pipelines for scalability, efficiency and cost-effectiveness
Troubleshoot and resolve issues related to model deployment and performance
Ensure compliance with security and data privacy standards in all MLOps activities
Keep up-to-date with the latest MLOps tools, technologies and trends

What skills and experience should you have?

Strong understanding of machine learning principles and model lifecycle management
Proficiency in programming languages such as Python, with hands-on experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-learn
Knowledge of CI/CD pipelines, automation tools and version control systems like Git
Strong problem-solving skills and ability to troubleshoot complex issues
Experience with monitoring tools and practices for model performance in production
Ability to work collaboratively in cross-functional teams
Experience with Google Cloud Platforms and their respective machine learning services
Familiarity with containerisation and orchestration tools such as Composer and Kubernetes
Knowledge and understanding of cloud data platform architecture, infrastructure, maintenance, and optimisation

What we offer you…

18% Bonus
Car allowance (or cash alternative)
Hybrid Working (typically 3 days in office)
25 days holiday (plus the option to buy more)
Plus, many more benefits!

Interview process and next steps…

We aim to move as quickly as possible! If your application is successful, you will be contacted by one of our recruitment team who will discuss the two-stage interview process with you.

Royal Mail is proud of our diverse employee network groups and the active role they play to support belonging and encourage a positive work environment. We are firmly committed to inclusion and passionate about our people representing the communities we serve.

We are happy to support your need for any adjustments during the application and hiring process. Please share the details within your application if required.

#J-18808-Ljbffr

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

Industry Insights

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

Jan 24, 2026

Education

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

Jan 18, 2026

Careers Jobs

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

Jan 13, 2026

Jobs

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.