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

Apply Now

Senior MLOps Engineer

Manchestertechfestival
Manchester
2 days ago
Create job alert

Date: September 2025
Company: Fuzzy Labs
Job Title: Senior MLOps Engineer
Location: Central Manchester
Working style: Hybrid


Fuzzy Labs are a Manchester based startup that helps our clients productionise machine learning through Open Source MLOps. We exist to help harness and channel the power of AI more quickly, to make positive change and use AI for good.


We are growing our team of engineers to help deliver an increasing number of client projects. You won’t need an existing MLOps background to apply for this role. You’ll learn as you go by immersing yourself in this exciting new field.


What we’re looking for

We’ve seen considerable growth over the past few years and this will continue as we build on our reputation as Open Source MLOps experts, while working with the community, and delivering solutions to our clients. The ideal candidate would be motivated to grow and progress in line with the company’s ambitions.


As well as being a great engineer, motivated by the chance to be at the forefront of MLOps adoption, you’ll also enjoy being part of a culture that values:



  • Loving what we do:a real passion for Open Source, MLOps and taking pride in great work.
  • Just trying it:MLOps is an exciting new field. We love to develop new skills, solve new problems and thrive on a challenge.
  • Being greater than the sum of parts:we are a team, one that isn’t just us but our customers and our community.
  • Positive impact:AI is going to change the world. We choose to use it for good and leave a positive legacy.

A typical day

You’ll work within an engineering squad and take part in every stage of the project delivery lifecycle, including:



  • Working directly with clients to understand their needs and recommend appropriate solutions.
  • Owning the delivery of key features (planning, architecting, coding, delegating).
  • Ensuring our high engineering standards are maintained and our clients are delighted.
  • Helping to run sprint planning sessions, retrospectives and code reviews.
  • Staying up-to-date with a fast-moving industry, embracing new tools and frameworks.
  • Coaching and mentoring junior members of your team.
  • Upskilling and coaching clients in MLOps.

As well as client work, we also set aside time to work on R&D projects and produce content in the form of blogs and videos. These projects are how we keep on top of a fast-moving technology landscape, in addition to being our greatest source of marketing. You’ll have the opportunity to craft your own voice in the MLOps community through R&D content.


Skills and Experience

Our team is made up of a mix of backgrounds. We are looking for smart, curious people who are always expanding their knowledge, and exploring new and emerging technologies. If you get excited about keeping up with the newest large language models, or figuring out how to scale generative models in the cloud, then you’ll fit right in.



  • Able to commute to our central Manchester office 3 days per week.
  • A degree in computer science, mathematics, or a science related subject.
  • A passion for coding, data science, and open source technologies.
  • Ability to write and review production-grade Python.
  • Experience with cloud computing, for example AWS, Google Cloud or Azure, along with modern DevOps practices and infrastructure-as-code tools.
  • Fluency in our core software tooling: Git, Unix/Linux, Docker. Plus, a strong opinion on your IDE / editor of choice is welcome 😉
  • Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn.
  • Excellent communication skills; both in customer-facing and internal team communication.
  • Knowledge of MLOps is not essential, but some awareness of this emerging space is good to have.

Due to the sensitive nature of some projects, you will be expected to undergo UK government security clearance after you start (at SC level – seethis link for more information). This will include a credit check and criminal records check.


By joining a small company you’ll have the chance to make a real impact on its future. There’s plenty of room for growth and we’ll work with you to help you realise your technical and personal ambitions because your success and the company’s success are one and the same.



  • 25 days holiday rising to 30 with service.
  • Equity option scheme, giving you a genuine stake in the business.
  • Cycle to Work Scheme.

Location

We run an organised hybrid scheme. On Monday, Wednesday and Thursday we work in the office, and use these days for the type of activities where being present in person brings benefits; for example sprint planning, project scheduling, retrospectives, social lunches and one to ones.


Interested?

If you’d like to work with us, send us a CV to the email address below and include a few words about yourself, and what interests you about the role and Fuzzy Labs. We love to see your passion for technology, so please include any links to your Github, Kaggle, tech blog, and cool side-projects if you have them.


#J-18808-Ljbffr

Related Jobs

View all jobs

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps Engineer

Senior MLOps 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.