Lead MLOPS Engineer

Hiscox
North Yorkshire
1 week ago
Create job alert
The team

The Lead MLOPS Data Engineer is a key role that sits in the Group Enterprise Systems (GES) data team and holds the responsibility of developing and leading a team of MLOPS engineers and owner the MLOPS chapter. In this role you’ll have accountability for developing standards, processes and deliverables for MLOPS across GES. You’ll work collaboratively across other chapter leads within GES as well collaborating with Data Scientists to bring ML models from development to production and ensure their ongoing performance.


You’ll be someone who enjoys leading and coaching others, challenging and defining new ways of working and making things happen. You will consider stakeholder management one of your strengths, with the ability to effectively engage with both business and technical stakeholders at all levels. You will thrive on ownership and autonomy, whilst also being an outstanding collaborator.


Job Type

Permanent


Build a brilliant future with Hiscox

Position: Lead MLOPS Data Engineer


Reporting to: Head of Data Engineering


Location: York


Band: Band II


Type: Permanent


The role
Responsibilities

A lead MLOps Data Engineer will build and maintain infrastructure for AI and ML models, focusing on data pipelines, automation, and deployment. This role bridges the gap between data engineering and machine learning, ensuring models are scalable, reliable, and monitorable in production. Key responsibilities include:



  • Demonstrate experience of leading a team of ML Engineers
  • Developing and maintaining infrastructure for deploying ML models in both real‑time and batch environments
  • Designing and implementing CI/CD pipelines
  • Creating and managing feature stores
  • Ensuring data quality
  • Collaborating with data scientists to productionize the models
  • Have experience of working with partners and business stakeholders
  • Must be able to work both independently and as a member of a team to deliver enterprise class data warehouse solutions

Technical Skills

  • Solid experience as a Python developer, ideally in a machine learning engineering context
  • Hands‑on experience of integrated cloud data science platforms particularly GCP or Databricks
  • Good understanding of core data science principles and understanding of challenges of migrating research code into production code
  • Strong understanding of software engineering best practice
  • Experience with infrastructure as code tools like Terraform
  • Experience with CI/CD tools and Git‑based development workflows
  • Understanding of API operations monitoring and logging
  • Strong problem‑solving skills and ability to work independently on technical tasks
  • Experience collaborating with technical and non‑technical team members in agile Scrum ceremonies – roadmap planning, feature workshops, backlog elaboration, code review

Our Nice To Haves

  • Insurance industry experience
  • Demonstrable experience in mentoring or supporting the development of junior team members

Behavioural

  • Intellect and gravitas to influence and gain credibility with stakeholders
  • Excellent written and verbal communication skills
  • Creative, proactive, logical and innovative – you do not accept the status quo
  • Highly results driven, with the energy and determination to succeed in a fast paced environment
  • Demonstrate a commitment to quality, service and personal ownership
  • Deal well with ambiguity and enable a consensus to be reached
  • An inquisitive mind‑set and desire to understand both data and business requirements
  • Continuous self‑improvement and learning

Diversity & Benefits

At Hiscox we care about our people. We hire the best people for the job and we’re committed to diversity and creating a truly inclusive culture, which we believe drives success.
Working life doesn’t always have to be in the office, so we have introduced hybrid working to encourage a healthy work life balance. This hybrid working model is set by the team rather than the business to enable you to manage your own personal work‑life balance.
We see it as the best of both worlds; structure and sociability on one hand, and independence and flexibility on the other.
Our benefits package includes a bonus, contributory pension, 25 days annual leave plus 2 Hiscox days and a 4 week paid sabbatical with every 5 years’ worth of service, private medical for all the family and much more.



#J-18808-Ljbffr

Related Jobs

View all jobs

Lead MLOps Engineer

Lead MLOps Engineer — Hybrid CI/CD & OpenShift

Lead MLOps Engineer for SageMaker Migration

Lead MLOps Engineer

Lead MLOps Engineer — Hybrid & Production AI

Lead MLOps Engineer — Scalable AI for Banking

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.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.