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Digital / data / automationDigital / data / automation
Machine Learning Engineer
DevOps engineers build and look after the computer systems that companies use to make and deliver software. They automate boring tasks, make sure servers run smoothly, and help developers get new features out to people as quickly and safely as possible.
No degree needed for many routesApprenticeship route
AI impact: medium££££ payApprenticeship route
55
AI impact
how much AI is reshaping it
Robin · your guide
Curious about being a machine learning engineer? Here's the honest picture - what you'd really do, what you'd earn, and every way in. No need to decide anything yet.
What you'd actually do
As a DevOps engineer, you work between software developers and the IT operations teams. You build the systems that let developers write code, test it, and push it live without things breaking. You use tools and automation to make this whole process faster and more reliable - so instead of manually checking everything by hand, machines do the checking for you.
Your days mix different kinds of work: you might be writing automation code, checking that servers are running well, fixing problems when something goes wrong, or working with cloud platforms like Amazon Web Services to keep everything running smoothly. You think about security and make sure that sensitive data stays safe. It is technical and hands-on work that needs you to understand both how software is built and how to run the computers it runs on.
1Collaborate with software developers and IT staff to oversee code releases.
2Implement automation tools and frameworks (CI/CD pipelines) to streamline operations.
3Monitor system performance, troubleshoot issues, and ensure high availability of services.
4Manage cloud infrastructure and services, optimizing for cost and performance.
5Maintain security protocols and compliance standards across all deployments.
6Conduct regular system tests and updates to ensure reliability and security.
7Document processes and create runbooks for operational procedures.
Career progression & pay
01
Getting in
Junior Machine Learning Engineer
£30,000 - £40,000
BSc in Computer Science, Mathematics, or related field
In this entry-level role, you will assist in developing machine learning models and gain hands-on experience with data preprocessing and algorithm implementation.
02
Building up
Mid-level Machine Learning Engineer
£50,000 - £70,000
3-5 years experience in machine learning and data analysis
As a mid-level engineer, you will take on more complex projects, lead small teams, and contribute to the strategic direction of machine learning initiatives.
03
At the top
Senior Machine Learning Engineer
£80,000+
10+ years experience, chartered status with BCS preferred
In this peak career stage, you will lead large-scale machine learning projects, mentor junior engineers, and drive innovation within your organisation.
Degrees that lead here via Engineering and Technology
Acoustics
Skyborne Aviation Training Limited
Advanced Manufacturing and Operations
Coleg Sir Gar
Aero-Mechanical Engineering
University of Strathclyde
Aero-Mechanical Engineering
University of Strathclyde
Aeronautical and Aerospace Engineering
The University of Leeds
Aeronautical and Aerospace Engineering
The University of Leeds
Apprenticeships that lead here
DevOps engineer
Digital
Level 4 · Higher2 yrs
Engineering operative
Engineering and manufacturing
Level 2 · GCSE level1 yrs
Powered pedestrian door installer and service engineer
Construction and the built environment
Level 2 · GCSE level2 yrs
Engineering and manufacturing foundation apprenticeship
Engineering and manufacturing
Level 2 · GCSE level0.7 yrs
Building service engineering foundation apprenticeship
Construction and the built environment
Level 2 · GCSE level0.7 yrs
Rail engineering operative
Engineering and manufacturing
Level 2 · GCSE level1 yrs
Who hires - top UK employers
DeepMind
A leader in AI research, DeepMind offers a dynamic environment for Machine Learning Engineers to work on groundbreaking projects.
BBC
The BBC is at the forefront of using machine learning to enhance media and broadcasting, providing exciting opportunities for engineers.
Ocado Technology
Ocado is revolutionising online grocery shopping through innovative technology, making it a great place for aspiring engineers.
Zalando
As a leading online fashion retailer, Zalando uses machine learning to enhance customer experience and optimise logistics.
Revolut
Revolut is a fintech company that leverages machine learning for fraud detection and customer insights, offering a fast-paced work environment.
AI & the future of this job
DevOps engineering sits in a genuinely interesting middle ground: AI is rapidly absorbing the more routine scripting, pipeline configuration, and incident triage work that once kept junior engineers busy, but the architectural judgement, cross-team negotiation, and security decision-making that define senior DevOps roles remain firmly human. Tools like GitHub Copilot, AWS CodeWhisperer, and emerging AI ops platforms are already writing Terraform, suggesting fixes, and auto-scaling infrastructure with minimal human input. The role is not disappearing, but it is compressing at the bottom end, meaning fewer entry-level positions and a steeper expectations curve for graduates. Those who treat AI tooling as a force multiplier rather than a threat will find genuine leverage in this field.
Within 5 Years
Significant workflow compression
By 2031, AI-assisted DevOps platforms will handle the majority of routine pipeline creation, log analysis, and incident response triaging with minimal human intervention. Junior DevOps roles will shrink noticeably as a single mid-level engineer augmented by AI tooling can cover what previously required a small team. The professionals still thriving will be those designing systems, setting reliability standards, and managing vendor and security complexity. Graduates entering now should plan to reach a meaningful specialism within two to three years rather than spending time on commodity tasks.
Within 10 Years
Role significantly restructured
By 2036, the traditional DevOps engineer title may largely dissolve into broader platform engineering and site reliability roles that require genuine architectural and business fluency. AI agents will manage most reactive operations autonomously, escalating only genuinely novel or high-stakes situations to humans. The engineers who remain central will function more like infrastructure strategists, setting the rules AI systems operate within rather than executing those rules themselves. This is a viable career, but it will look quite different from the role advertised today.
Within 20 Years
Deeply transformed, specialist-led
By 2046, autonomous infrastructure management will be the default across most UK tech organisations, with AI handling provisioning, scaling, security patching, and performance tuning end-to-end in mature environments. Human DevOps expertise will concentrate in highly regulated industries such as finance, defence, and healthcare, where accountability, compliance, and novel architecture decisions require human sign-off. The field will not be extinct, but it will be a specialist discipline rather than a broad hiring category. Those who build deep expertise in AI governance, cloud security, or critical national infrastructure will remain genuinely indispensable.
How to stay ahead
Pursue platform engineering over pure tooling
Platform engineering focuses on building the internal systems that developer teams consume, requiring architectural thinking and product instincts that AI cannot replicate today. This specialism is growing in demand across UK scale-ups and enterprises because it demands human judgement about developer experience and organisational workflow. It positions you above the automation layer rather than inside it.
Build deep cloud security expertise
Security is the one DevOps domain where AI assistance amplifies rather than replaces human responsibility, because the consequences of errors are too serious for full automation to be trusted without oversight. Certifications such as AWS Security Specialty, the CKS for Kubernetes, or SC-100 on Azure carry genuine market weight and will continue to do so. Regulators, insurers, and boards all require human accountability in this space, which is a structural protection against automation.
Learn to direct AI agents, not just use tools
The DevOps engineers with leverage in the near future will be those who can design, constrain, and audit AI-driven automation pipelines rather than simply running scripts manually. Practise building workflows with tools like Pulumi Automation API, Backstage, or AI ops platforms so you understand their failure modes and limits. This metacognitive skill, knowing when to trust AI output and when to override it, is what employers will actually pay for.
Develop commercial and reliability fluency
Understanding the business cost of downtime, the trade-offs in cloud spend, and how SLAs translate to engineering decisions separates DevOps engineers who get promoted from those who get automated away. FinOps principles, service level objective design, and incident post-mortem facilitation are skills that require human context and stakeholder communication. Adding this commercial layer to technical ability makes you a practitioner who can own outcomes, not just execute tasks.
How to get in - your routes
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