Senior Machine Learning Operations Engineer

First Central Services UK Ltd
Manchester
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

On Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Data Scientist - Payments

We’re 1st Central, a market-leading insurance company utilising smart data and technology at pace. Rapid growth has been based on giving our 1.4 million customers exactly what they want: great value insurance with an excellent service. And that’s the same for our colleagues too; we won Insurance Employer of the Year at the British Insurance Awards 2024 and our Glassdoor score is pretty mega too!

We’re big on data: it gives us the insights we need to give the right cover to the right customers at the right price. But it’s our people inside and outside the business who truly power us. Were currently on the hunt for an experienced Senior Machine Learning Operations Engineer to join our Data Function.

You’ll play a significant role within our Data & Analytics Function, working on the design and implementation of machine learning model engineering frameworks, solutions, and best practices. You’ll be technically proficient in machine learning and its applications; you’ll demonstrate an understanding of data management and show a keen interest in keeping up with industry trends. You’ll work closely with different teams such as Data Science, Data Engineering, and Software Development to ensure efficient operation and use of Data Science models. You will facilitate the full life cycle of machine learning models from data ingestion, model development, testing, validation, deployment, to monitoring and retraining of models within different environments.

This is a flexible hybrid role, with occasional visits to our offices in Salford Quays (Manchester) or Haywards Heath (West Sussex) when required. For those based further afield, we also welcome applications from remote UK based‑ workers. We offer excellent flexibility in working patterns and a company‑wide culture you can be proud to be part of.

If you possess a strong understanding of applying MLOps frameworks in production, combined with a data engineering background and experience in Databricks and PySpark, we want to hear from you.

Core skills were looking for to succeed in the role:

Technical Skills: Comprehensive knowledge of Databricks, PySpark, Microsoft Azure (Azure ML, Azure Stream Analytics, Cognitive Services, Event Hubs, Synapse, Data Factory).

Data Science & Programming Skills: Fluency in Python and modelling frameworks such as PyTorch and TensorFlow.

ML Ops Expertise: You’ll be skilled in deploying and managing Machine Learning Models within a production environment.

Analytical & Problem-Solving: Excellent problem-solving and analytical skills, with the ability to diagnose and troubleshoot problems quickly.

Organisational Skills: Strong time management and organisational abilities, experience working to tight deadlines.

Communication & Collaboration: Excellent communication skills, both verbal and written, ability to collaborate effectively with cross-functional teams.

What’s involved:

You’ll contribute to the design and implementation of Machine Learning Engineering standards and frameworks. You’ll support model development, with an emphasis on auditability, versioning, and data security. You’ll implement automated data science model testing and validation. You’ll assist in the optimisation of deployed ML model scoring code in production services. You’ll assist in the design and implementation of data pipelines and engineering infrastructure to embed scaled machine learning solutions. You’ll use CI/CD pipelines, manage the deployment and version management of large numbers of data science models (Azure DevOps). You’ll support the implementation of Machine Learning Ops on cloud (Azure & Azure ML. Experience with Databricks is advantageous.) You’ll protect against model degradation and operational performance issues through the development and continual automated monitoring of model execution and model quality. You’ll manage automatic model retraining within a production environment. You’ll engage in group discussions on system design and architecture, sharing knowledge with the wider engineering community. You’ll collaborate closely with data scientists, data engineers, architects, and the software development team. You’ll liaise with stakeholders across the business to ensure ML is being used to improve strategic business decisions and identify new areas for improvements. You’ll adhere to the Group Code of Conduct and Fitness and Propriety policies, Company Policies, Values, guidelines, and other relevant standards/ regulations at all times.

Job-specific competencies

Experience in developing and maintaining production ML systems, including automatic model retraining and monitoring of production models. Deploying Infrastructure as Code (IAC) across various environments such as dev, uat and prod Handling large volumes of data in various stages of the data pipeline, from ingestion to processing Proven experience with feature stores, using them for both offline model development and online production usage. Building integrations between cloud-based systems using APIs, specifically within the Azure environment Practical knowledge of agile methodologies applied in a data science and machine learning environment. Designing, implementing, and maintaining data software development lifecycles, with a focus on continuous integration and deployment (CI/CD) Demonstratable expertise in machine learning methodology, best practices, and frameworks Understanding of microservices architecture, RESTful API design, development, and integration Basic understanding of networking concepts within Azure Familiarity with Docker and Kubernetes is advantageous. Experience within financial/insurance services industry is advantageous. Experience with AzureML and Databricks is advantageous.

Skills & Qualifications

Strong understanding of Microsoft Azure, (Azure ML, Azure Stream Analytics, Cognitive services, Event Hubs, Synapse, and Data Factory) Fluency in common data science coding capabilities such as Python and modelling frameworks such as Pytorch, Tensorflow etc. Skilled in application of MLOps frameworks within a production environment Excellent communication skills, both verbal and written Strong time management and organisation skills Ability to diagnose and troubleshoot problems quickly. Excellent problem-solving and analytic skills

Behaviours

Embrace, embed and incorporate the company values. Self-motivated and enthusiastic An organised and proactive approach Strong stakeholder management Ability to work on own initiative and as part of a team. A flexible approach and positive attitude Strives to drive business improvements to contribute to the success of the business.

This is just the start. Imagine where you could end up! The journey’s yours… 

What can we do for you?

People first. Always. We’re passionate about our colleagues and know the best people deserve an extraordinary working environment. We owe it to them so that’s what we offer. Our workplaces are energetic, inspirational, supportive. To get a taste of the advantages you’ll enjoy, take a look at all our perks in full .

Intrigued? Our Talent team can tell you everything you need to know about what we want and what we’re offering, so feel free to get in touch.

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

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

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

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

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.