UK

United Kingdom · Technology and data · Senior (5-8 years)

Senior Model Reliability Engineer

Here is the whole job, in plain words. What it is, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

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  • Experience bandSenior (5-8 years)
  • Direct reportsNo direct reports
  • Reports toLead Model Reliability Engineer or Model Reliability Engineering Manager
  • UK framework levelUsually a manager, or the deepest specialist in a team

Also advertised as Lead MLOps Engineer · Senior ML Platform Engineer · Senior SRE (Machine Learning)

Built on 43,079 real UK job descriptions we analysed · grounded in qualifications employers recognise

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence OCN London · Level 5 · covers 1 of 1 standard
ATHE Level 5 Extended Diploma in Computing ATHE Ltd · Level 5 · covers 1 of 1 standard
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 1 of 1 standard
Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 1 of 1 standard
Qualifi Level 3 Extended Diploma in IT and Data Science Qualifi Ltd · Level 3 · covers 1 of 1 standard
Qualifi Level 3 Diploma in Data Science Qualifi Ltd · Level 3 · covers 1 of 1 standard

Matched from the standards this job is measured against: a suggestion, not an official ruling.

The units you would start with

Machine Learning Algorithms OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence
Data Analytics and Machine Learning ATHE Level 5 Extended Diploma in Computing
Machine Learning Pearson BTEC Level 5 Higher National Diploma in Computing for England
Machine Learning Methods and Models in Data Science Qualifi Level 3 Extended Diploma in IT and Data Science

The future skills this job leans on

AI Fluency working with AI as a thinking partner
Data Fluency turning numbers into a decision
AI Governance Fluency using AI responsibly
Automation Fluency building AI into how work runs
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Senior Model Reliability Engineer

4 units that map to this job, from the qualifications that cover it.

  1. Machine Learning Algorithms OCN London · covers 1 of 1 standards Level 5
  2. Data Analytics and Machine Learning ATHE Ltd · covers 1 of 1 standards Level 5
  3. Machine Learning Pearson Education Ltd · covers 1 of 1 standards Level 5
  4. Machine Learning Methods and Models in Data Science Qualifi Ltd · covers 1 of 1 standards Level 3
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

2AI and this work

Will AI change your work as a Senior Model Reliability Engineer?

Nobody can say exactly how. Here is what this job is being asked for next, and where to start.

The new skills this role is being asked for

  • Prompt Engineering & LLM Integration for MLOps
  • ML Model Security & Adversarial Robustness
Why each one is rising

The future skills this job leans on

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

What happens when you start

Next, a free account. We build your Senior Model Reliability Engineer plan as you set up, starting with a Future Fluency module on using AI in your job. No card.

Your first Future Fluency module is free. After that, your AI tutor is £70 a month.

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3What this role really is

A Senior Model Reliability Engineer keeps machine learning models running correctly in production and fixes them when they fail.

4What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

AWS (Lambda, EKS, Step Functions) Advanced

Designing and deploying serverless functions for model inference, managing Kubernetes clusters for model serving, and orchestrating complex ML workflows.

Kubernetes (Helm, Ingress) Advanced

Designing and managing K8s clusters for model serving, writing custom controllers, building Helm charts for application deployment, and managing Ingress for external access.

Prometheus & Grafana Advanced

Writing complex PromQL queries, building new, comprehensive Grafana dashboards for model health, configuring advanced alerting rules and thresholds, and debugging production issues.

GitLab CI / GitHub Actions Advanced

Designing and building new CI/CD pipelines for complex model deployments, including multi-stage canary releases, automated testing, and infrastructure provisioning.

Terraform / CloudFormation Advanced

Authoring complex Infrastructure as Code (IaC) modules, managing state, and implementing secure workflows for deploying and managing cloud resources.

Python (pandas, NumPy, scikit-learn, FastAPI) Advanced

Writing robust, production-grade code for model serving, data validation, monitoring agents, and automation scripts. You'll need to understand model code, even if you're not building the models themselves.

5What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing in Where you are nowThe step above
Technical Approach for Model Deployment Executes defined deployment steps under supervision; raises questions about deviations. Chooses between standard deployment patterns (e.g., blue/green, A/B) for routine models; consults on novel approaches. Designs and implements advanced deployment strategies (e.g., canary releases, shadow deployments) for critical models; makes recommendations for team-wide adoption.
Incident Resolution Strategy (P2 Incident) Follows runbooks to diagnose and fix known issues; escalates if runbook fails or issue is novel. Independently diagnoses and resolves P2 incidents using existing tools and knowledge; may consult on complex root causes. Leads the incident response for P2 incidents, coordinating multiple teams if needed; defines the root cause analysis and proposes long-term preventative measures.
Monitoring Tool Selection for New Model Uses existing monitoring tools and dashboards; reports on gaps. Configures new dashboards and alerts within existing monitoring stack (Prometheus/Grafana) for new models. Evaluates and recommends new monitoring tools or features for specific model types; designs the integration and implementation strategy for these tools.
Budget for MLOps Tooling No authority; requests tools through supervisor. Can request licences for established tools; needs approval for new software spend. Recommends specific MLOps tools or services up to £5K; requires manager approval for larger expenditures, but your input carries significant weight.

6How you'll be judged

The scoreboard, and what is not on it. The hard targets, how often each is actually looked at, and the quiet signals that never reach a dashboard.

Model Availability
The percentage of time our critical models are up and running, making predictions without issues.
Target · 99.95% uptime for critical models

If a model is down for 20 minutes in a month, that's a miss. We track this rigorously because every minute counts.

SLO Adherence
How often our models meet their defined Service Level Objectives for latency, throughput, and prediction quality.
Target · 95% adherence to all defined SLOs

If the fraud detection model's P99 latency is consistently above 100ms when the SLO is 80ms, that's a problem you'll need to solve.

Reduction in Production Incidents & Rollbacks
The number of times we have to roll back a new model deployment or fix a production issue caused by a model.
Target · Reduce incidents by 20% quarter-over-quarter; less than 5% of deployments require a rollback.

You'll be credited for designing a canary deployment strategy that cut production-impacting incidents from new model versions by 75%.

Mean Time To Resolution (MTTR) for P1/P2 Incidents
How quickly we can identify and fix critical (P1) and major (P2) model-related production issues.
Target · Average MTTR under 30 minutes for P1, under 2 hours for P2.

Leading an incident where a critical model went down and getting it back up in 25 minutes, despite it being 2 AM, shows you're hitting this target.

Incident Post-Mortem Quality & Learning
The thoroughness and actionable insights from your post-mortems after an incident. It's not just about what happened, but what we learned and what we'll do to stop it from happening again.
  • Post-mortems are blameless, clearly identify root causes (not just symptoms), lead to concrete action items, and are shared effectively with relevant teams. You're seen as the person who drives real systemic improvements after an outage.
Mentee Development & Team Capability
How effectively you guide and upskill junior and mid-level engineers, helping them tackle more complex problems and grow their own reliability skills.
  • Your mentees are taking on more challenging tasks independently, their code quality improves, and they actively seek your advice. You'll be recognised for helping them achieve promotions or take on lead roles in projects.
Cross-Team Collaboration & Influence
Your ability to work with and influence data scientists, product managers, and other engineering teams to adopt better reliability practices.
  • Data Science teams proactively involve you in design discussions for new models. Product teams understand and respect the need for robust testing and staged rollouts. You're seen as a trusted technical advisor, not just 'the person who fixes things'.

7Would you like it

Both sides. What people enjoy, and what grinds them down.

What people enjoy
Solving Complex Technical Puzzles

You'll spend a good chunk of your week digging into logs, tracing network calls, and debugging obscure errors across different systems. There's a real satisfaction in unravelling a knotty problem that no one else could figure out.

Diagnosing why a model's predictions suddenly became erratic, only to discover it was a subtle data type mismatch between a legacy system and a new microservice.

Building Robust, Scalable Systems

You'll get to design and implement infrastructure that can handle millions of requests, ensuring our models stay up even under heavy load. You'll be building the guardrails and automation that make our ML platform truly resilient.

Architecting a new model serving platform using Kubernetes and Istio to ensure zero downtime deployments and automatic scaling.

Mentoring and Growing Others

You'll regularly review code, pair program, and explain complex concepts to junior engineers. Seeing them 'get it' and start solving problems independently will be a big part of your reward.

Helping a junior engineer debug their first production incident, guiding them through the steps without just giving them the answer, and seeing them confidently resolve it.

What frustrates people
  • The 'Notebook-to-Prod' Problem: Getting a 2,000-line Jupyter Notebook with hardcoded paths and zero tests, with the expectation of making it a scalable, real-time production service by Friday. Honestly, it's a nightmare.
  • Silent Failures: Models that don't crash, but silently degrade, making increasingly bad predictions for weeks until a customer complains or a business metric tanks. These are far harder to debug than a server that's on fire.
  • On-Call for Black Boxes: Being responsible for the 24/7 uptime of a model whose internal logic is a complete mystery, making it nearly impossible to reason about failures. It's like being asked to fix a car engine you've never seen before.
  • Fighting for Observability: The constant battle to convince data scientists to add proper logging, metrics, and instrumentation to their model code instead of just using `print()` statements. It's exhausting.
  • Data Quality Nightmares: Spending 80% of an incident investigation tracing a model failure back to a subtle schema change or data quality issue in an upstream source owned by a different team. It's not your fault, but it's your problem.
  • The Scapegoat Factor: When the model's predictions are blamed for a dip in business performance, you're often the first person called, even if the model is operating perfectly and the market simply changed. You'll need a thick skin.
What this role does not give you
  • A predictable 9-to-5 routine – production incidents don't care about your schedule.
  • The ability to always see every piece of your work make it to production and be celebrated. Many of your best reliability improvements will go unnoticed because they prevent problems that never happen.
  • Complete control over all aspects of the ML lifecycle; you'll often be reliant on other teams for data or model changes.

8Who you work with

Your work directly influences the stability and performance of our revenue-generating and cost-saving machine learning models. Get it right, and we protect millions in revenue. Get it wrong, and we face downtime, customer dissatisfaction, and potentially significant financial losses. You're a key player in ensuring our AI investments actually pay off.

Inside the business
  • Data Science Leads (they build the models, you make them production-ready)
  • Product Managers (they care about model performance and new features)
  • Software Engineering Teams (you'll work with them on integration points)
  • Operations Team (they'll be your first call when something goes wrong at 3 AM)
  • Director of MLOps / Head of AI (they'll expect you to deliver)
Outside the business
  • Cloud Providers (AWS, GCP, Azure)
  • MLOps Tool Vendors (e.g., Arize, Fiddler, WhyLabs)
  • External auditors (occasionally, for model governance)

9What you need before you start

Not a wish list. The things you would be expected to already have.

  • At least 5 years of hands-on experience in MLOps, Site Reliability Engineering (SRE), DevOps, or a similar role focused on production systems.
  • Proven track record of designing, building, and operating highly available and scalable systems, ideally with a focus on machine learning workloads.
  • Strong programming skills in Python, with experience writing production-grade code, not just notebooks.
  • Deep expertise with a major cloud provider (AWS preferred) and containerisation technologies (Docker, Kubernetes).
  • Experience leading incident response and performing thorough root cause analysis for complex technical issues.
  • Demonstrable experience mentoring junior engineers and influencing technical decisions.

10What to practise next

Where the job is going, and what to do about it starting this week.

Advanced Cloud Native Architecture (Multi-Cloud & Edge)

While we're primarily on AWS, the reality is that some services might live on other clouds or even at the edge (e.g., IoT devices). You'll need to think beyond a single cloud provider and consider how to build resilient, portable, and cost-optimised ML systems across diverse environments.

Cloud cost optimisation strategies for ML workload · Hybrid cloud deployments for ML (on-premise data w · Edge ML deployment patterns (model compression, on · Serverless ML inference patterns (Lambda, Google C · Service mesh integration (Istio, Linkerd) for adva

  • This quarter: Explore GCP's Vertex AI or Azure ML capabilities, even if it's just in a sandbox environment.
  • Next quarter: Research cost optimisation techniques for your current AWS ML workloads and propose improvements.
  • Within 6 months: Investigate and experiment with a service mesh for one of our Kubernetes-deployed models.
  • Within 9 months: Present a proposal for a multi-cloud or hybrid ML deployment strategy for a specific use case.

Quick win: Review your current cloud spend for ML inference. Are there obvious areas for optimisation you can tackle this week?

MLOps Platform Engineering & Tooling Integration

As we scale, we can't have every team building their own MLOps stack. You'll be instrumental in evolving our MLOps platform, integrating tools like MLflow, Kubeflow, or Tecton into a cohesive, self-service platform that data scientists can use easily and reliably.

ML feature stores (e.g., Tecton, Feast) for consis · Model registries (e.g., MLflow, SageMaker Model Re · Experiment tracking platforms (e.g., Weights & Bia · Data versioning and lineage tools (e.g., DVC, Delt · Building internal MLOps platforms as a product for

  • This month: Deep dive into one MLOps platform tool (e.g., MLflow) and understand its full capabilities.
  • Next quarter: Identify a pain point for our data scientists related to model lifecycle management and propose a tooling solution.
  • Within 6 months: Lead the integration of a new MLOps tool into our existing platform, ensuring it's easy for data scientists to use.
  • Within 9 months: Develop a training session or documentation for data scientists on how to effectively use our MLOps platform.

Quick win: Talk to a few data scientists about their biggest MLOps frustrations. What 'low-hanging fruit' tooling improvement could you make for them this month?

11Staying current once you are in

What people here do to keep up
  • Regularly contributing to open-source MLOps projects or communities.
  • Attending MLOps conferences (e.g., MLOps World, KubeCon) and sharing key learnings with the team.
  • Subscribing to and actively reading MLOps and SRE blogs and newsletters (e.g., Google SRE Blog, Martin Fowler's bliki).
  • Taking online courses on advanced distributed systems, cloud architecture, or specific MLOps tools.
  • Presenting on technical topics internally or at local meetups.

12The rising capability

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration for MLOps

Honestly, LLMs are already changing how we work. Competitors are using tools like GPT to draft RCA reports in 10 minutes that used to take 2 hours. MREs who figure out how to effectively use and integrate LLMs into their workflows will outproduce their peers by a significant margin. This isn't future-gazing; it's happening now.

  • Context windows and token limits (understanding wh
  • Temperature settings for different tasks (creativi
  • RAG (Retrieval Augmented Generation) architectures
  • Output validation and hallucination detection (kno
  • Prompt chaining for complex analysis (breaking dow

ML Model Security & Adversarial Robustness

As ML models become more critical, they also become bigger targets. We're seeing more sophisticated attacks on models (e.g., data poisoning, adversarial examples) that can lead to incorrect predictions or data breaches. Protecting our models isn't just about uptime, it's about integrity and security.

  • Data poisoning attacks (manipulating training data
  • Model inversion attacks (reconstructing training d
  • Adversarial examples (subtly altering inputs to fo
  • Secure ML deployment practices (e.g., model encryp
  • Explainable AI (XAI) for security audits (understa

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • MLOps Principles
  • Drift & Skew Detection
  • SLO-Driven Monitoring
  • Advanced Deployment Strategies
  • Incident Management & Root Cause Analysis (RCA)
  • System Design for ML

14The pathway

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Model Reliability Engineer (L2)

    2-3 years as an MRE

    Skills to master

    • Independent incident resolution, building comprehensive monitoring, automating routine deployments, understanding model lifecycle.

    You're ready to move on when

    • Consistently resolving P2 incidents without escalation.
    • Successfully implementing new monitoring for several models.
    • Proactively identifying and fixing potential reliability issues.
    • Mentoring new joiners informally and providing solid code reviews.
  2. 2

    Senior Site Reliability Engineer (SRE) or DevOps Engineer

    5+ years in SRE/DevOps

    Skills to master

    • Deep understanding of ML-specific challenges (drift, skew, model serving), Python programming for ML, MLOps tooling.

    You're ready to move on when

    • Strong background in distributed systems, cloud infrastructure, and CI/CD.
    • Demonstrable interest and some experience with machine learning concepts or platforms.
    • Ability to quickly pick up new domains and apply SRE principles to ML.
    • Proven track record of building and operating highly available systems.
  3. 3

    Senior Data Engineer or ML Engineer

    5+ years in Data/ML Engineering

    Skills to master

    • Deep dive into production operations, incident management, infrastructure as code, advanced monitoring, and system resilience.

    You're ready to move on when

    • Strong programming skills and understanding of data pipelines.
    • Experience deploying and managing ML models in production (even if not full MLOps).
    • Desire to specialise in operational excellence and system reliability.
    • Comfortable with on-call rotations and incident response.

15Where this role leads

The long view: This work can lead to principal engineering roles focused on ML systems reliability, or into management as a Director of MLOps and Reliability.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Senior Model Reliability Engineer is actually changing.

Where it leads

Roles this leads to

The record names a next step, but the catalogue has no page for it yet:

  • Staff Model Reliability Engineer (L4)
  • Model Reliability Engineering Manager (L5)

How you would learn this

One to one, on the work you already do

A tutor sets the task against your own job and marks the reasoning, at your pace. There is no cohort to fall behind.

A lesson from Machine Learning Algorithms, worked against Senior Model Reliability Engineer.

Machine Learning Algorithms Level 5

Applied to your work in Senior Model Reliability Engineer

This unit aims to provide learners with a comprehensive understanding of machine learning, covering its concepts, principles, and techniques, including a range of machine learning algorithms and relevant programming libraries. Learners will also understand appropriate solutions for evaluating artificial intelligent tasks using various tools, methods and techniques.

How the thinking builds
  1. 1 Remember
  2. 2 Understand
  3. 3 Apply
  4. 4 Analyse
  5. 5 Evaluate
  6. 6 Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

It is £70 a month, billed monthly. Your free first module is Future Fluency, and you can cancel any time.

Start my Senior Model Reliability Engineer plan, free
See Your Progress Grow Illustration
Senior Model Reliability Engineer
  • MLOps Principles
  • Drift & Skew Detection
  • SLO-Driven Monitoring
  • Advanced Deployment Strategies
  • Incident Management & Root Cause Analysis (RCA)
  • System Design for ML
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

16The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Senior Model Reliability Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Staff Model Reliability Engineer (L4)

    3-5 years as a Senior MRE

    You'll move from leading specific critical models to architecting core MLOps infrastructure that multiple teams use. You'll solve cross-team reliability challenges and set technical direction.

    • Designing enterprise-grade MLOps platforms (e.g., self-service model deployment)
    • Deep expertise in cloud cost optimisation for ML at scale
    • Advanced security patterns for ML systems
    • Evaluating and integrating new MLOps technologies for the entire organisation

  2. Model Reliability Engineering Manager (L5)

    3-5 years as a Senior MRE

    This path shifts you into people leadership. You'll be responsible for hiring, managing, and developing a team of MREs, setting their objectives, and guiding their career growth.

    • Defining team strategy and roadmap for model reliability
    • Building a high-performing MRE team culture
    • Managing vendor relationships for MLOps tools
    • Representing the MRE function in broader engineering leadership forums
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be honest, a good chunk of Model Reliability Engineering can be repetitive or involve sifting through mountains of data. But what if AI could handle the grunt work, freeing you up to focus on the really interesting, complex problems? That's exactly what's happening.

We're not just talking about hypothetical future tech. Our team is already using AI to make daily tasks faster, smarter, and less tedious. Imagine less time compiling reports, less time digging through logs, and more time actually architecting robust systems. Here's how AI could turbocharge your work as a Senior Model Reliability Engineer:

Automated RCA Drafts

After an incident, an LLM can parse all the messy incident data—Slack channels, alert timelines from Prometheus, deployment logs from GitLab—and spit out a first-draft Root Cause Analysis (RCA) document. It'll even suggest a timeline and potential contributing factors. You'll just need to review and refine it, not start from a blank page.

Drift Summary Generator

Instead of manually sifting through raw drift monitoring data (from tools like Arize or Fiddler), an AI agent can analyse it and automatically generate a concise, human-readable weekly summary. It'll highlight the top 3 features with the most significant drift and even suggest their potential business impact. No more staring at endless charts.

IaC Optimization Assistant

Imagine an AI code assistant, trained on all the AWS Well-Architected Framework best practices, scanning your Terraform or CloudFormation code. It'll suggest specific optimisations for cost, security, and reliability, preventing common misconfigurations before they hit production. It's like having an expert peer review your infrastructure in seconds.

On-Call Knowledge Bot

During a high-pressure incident, you can ask a fine-tuned LLM (trained on our internal Confluence docs, runbooks, and past incident reports) specific questions. 'What's the runbook for a P1 latency spike on the fraud model?' or 'What's the typical fix for a data drift alert on the recommendation engine?' Instant, contextual answers, reducing panic and MTTR.

Common questions

Common questions

How do you become a Senior Model Reliability Engineer?

Common routes in include Model Reliability Engineer (2-3 years as an MRE), Senior Site Reliability Engineer (SRE) or DevOps Engineer (5+ years in SRE/DevOps) and Senior Data Engineer or ML Engineer (5+ years in Data/ML Engineering). Times vary with prior experience.

Where can a Senior Model Reliability Engineer progress to?

This role can lead on to Staff Model Reliability Engineer (3-5 years as a Senior MRE) and Model Reliability Engineering Manager (3-5 years as a Senior MRE), depending on the skills you build.

What new skills matter most for a Senior Model Reliability Engineer?

Increasingly, Prompt Engineering & LLM Integration for MLOps and ML Model Security & Adversarial Robustness. These are the skills our record of this role lists as growing.

Free tools

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Three free tools, no account needed. Each one ends with a role, a level and a way in.

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17Where to go from here

Other roles at Level 5

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

 More jobs in technology and data

Stay in the field you know and move sideways rather than up.
If you leave this industry

The skills involved carry into broader Site Reliability Engineering, ML Platform Engineering, Data Engineering leadership, or a Chief Technology Officer track in companies built around AI.

This role profile is © 2026 Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Start my Senior Model Reliability Engineer plan, free What happens next: a free account, then we build your plan as you set up. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.