Machine Learning Operations Lead

Bristol
6 months ago
Applications closed

Related Jobs

View all jobs

MOTOR INSURANCE PRICING PRACTITIONER / DATA SCIENTIST

Capacity Planning Data Scientist

Machine Learning Engineer (Forward Deployed)

Genomic Data Scientist in Rare Disease (we have office locations in Cambridge, Leeds & London)

Data Science Consultant

Data Scientist

Lead Machine Learning Operations Engineer - Remote - £70-£90k + Excellent Benefits

We're seeking a Lead Machine Learning Operations Engineer to spearhead the development and optimisation of our cutting-edge data platform. This is a strategic, hands-on leadership role where you'll guide a growing ML Ops team, architect scalable infrastructure, and ensure seamless deployment and monitoring of machine learning models in production.

What you'll be doing as Lead ML Ops Engineer:

Leading the design and implementation of robust ML Ops pipelines using Azure, Databricks, and Delta Lake
Architecting and overseeing API services and caching layers (e.g., Azure Cache for Redis)
Driving integration with cloud-based data storage solutions such as Snowflake
Collaborating with data scientists, engineers, and product teams to align ML infrastructure with business goals
Establishing best practices for model deployment, monitoring, and lifecycle management
Conducting performance tuning, load testing, and reliability engineering
Managing CI/CD workflows and infrastructure as code via Azure DevOps and GitHub
Mentoring junior engineers and fostering a culture of technical excellence and innovation

What we're looking for from the Machine Learning Operations Lead:

Proven experience in ML Ops leadership, with deep expertise in Azure, Databricks, and cloud-native architectures
Strong understanding of Postgres, Redis, Snowflake, and Delta Lake Architecture
Hands-on experience with Docker, container orchestration, and scalable API design
Excellent communication and stakeholder management skills
Ability to drive strategic initiatives and influence technical direction
Bonus: experience with Azure Functions, Azure Containers, or Application Insights

Benefits for the Machine Learning Operations Engineer:

25 days holiday (rising with service) + bank holidays
Annual discretionary bonus
Enhanced pension scheme
Flexible working and flexi-time options
Healthcare cash plan
Electric vehicle salary sacrifice scheme
Discounts scheme
Wellbeing app
Enhanced maternity and paternity leave
Life assurance (4x salary)
Cycle to Work scheme
Employee referral scheme

If you are interested in this position please click 'apply'.

Hunter Selection Limited is a recruitment consultancy with offices UK wide, specialising in permanent & contract roles within Engineering & Manufacturing, IT & Digital, Science & Technology and Service & Sales sectors.

Please note as we receive a high level of applications we can only respond to applicants whose skills & qualifications are suitable for this position. No terminology in this advert is intended to discriminate against any of the protected characteristics that fall under the Equality Act 2010.

For the purposes of the Conduct Regulations 2003, when advertising permanent vacancies we are acting as an Employment Agency, and when advertising temporary/contract vacancies we are acting as an Employment Business

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.

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

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.