UK

United Kingdom · Technology and data · Director/VP (16-20 years)

Director of ML Operations

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 bandDirector/VP (16-20 years)
  • Direct reports25-100+ reports
  • Reports toVP of Technical Operations
  • UK framework levelUsually a director, accountable for a division and its numbers

Also advertised as Head of Production ML Support · VP of ML Reliability · Director of Technical Operations (Machine Learning)

Built on 43,079 real UK job descriptions we analysed · grounded in what real qualifications cover

Two short quizzes about you

1From this job to a course

The qualifications matched to this job

OTHM Level 7 Diploma in Data Science OTHM Qualifications · Level 7 · covers 1 of 2 standards
ATHE Level 7 Extended Diploma in Computing ATHE Ltd · Level 7 · covers 1 of 2 standards
NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 1 of 2 standards
Level 6 Award in Data-led Decision Making Institute of Sales Professionals · Level 6 · covers 1 of 2 standards
OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence OCN London · Level 5 · covers 1 of 2 standards

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

The units you would start with

Data Science Foundations OTHM Level 7 Diploma in Data Science
Applications of Machine Learning and Artificial Intelligence ATHE Level 7 Extended Diploma in Computing
Introduction to Data Science and Big Data NCC Education Level 5 Extended Diploma in Computing with Data Science
Data-led Decision Making Level 6 Award in Data-led Decision Making
Apply the Concepts of Data Science to Computer Engineering NOCN Level 5 Diploma in Computer Engineering (Information Technology and Data Science)
Machine Learning Algorithms OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence

The future skills this job leans on

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

Built for Director of ML Operations

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

  1. Data Science Foundations OTHM Qualifications · covers 1 of 2 standards Level 7
  2. Applications of Machine Learning and Artificial Intelligence ATHE Ltd · covers 1 of 2 standards Level 7
  3. Introduction to Data Science and Big Data NCC Education Limited · covers 1 of 2 standards Level 5
  4. Data-led Decision Making Institute of Sales Professionals · covers 1 of 2 standards Level 6
  5. Apply the Concepts of Data Science to Computer Engineering NOCN · covers 1 of 2 standards Level 5
  6. Machine Learning Algorithms OCN London · covers 1 of 2 standards Level 5
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 Director of ML Operations?

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

  • AI Governance & Responsible AI Operations
  • Advanced FinOps for ML
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 Director of ML Operations plan as you set up, starting with the free Automation Fluency module. No card.

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Keep this read. One email with what AI is changing in this job, the new skills it asks for and where to start free. Nothing else follows unless you confirm your address.

3What this role really is

A Director of ML Operations manages the operational health of machine learning models in production, keeping them reliable, performant and secure.

4What you'd actually use

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

Jira Service Management / ServiceNow (Strategic) Expert

Defining and optimising enterprise-wide ITSM workflows, integrating with other systems, and using advanced reporting for strategic decision-making and resource planning.

Datadog / Grafana / Prometheus (Architectural) Architect

Defining the enterprise monitoring strategy, evaluating and selecting new tools, and ensuring end-to-end observability across all ML services. You'll ensure your teams have the right data at their fingertips.

Splunk / ELK Stack (Strategic Oversight) Strategic

Managing logging infrastructure, defining data retention policies, controlling costs associated with log ingestion, and ensuring compliance. You'll ensure your teams can get to the logs they need, quickly and efficiently.

Python (Automation & Strategy) Advanced

Overseeing the development of internal support tooling, championing automation initiatives across the team, and understanding the capabilities and limitations of scripting for operational tasks.

AWS / GCP (Cloud Strategy & Governance) Strategic

Involved in architectural decisions for reliability, managing cloud support budgets, defining security protocols for support access, and negotiating cloud contracts.

Confluence / Notion (Knowledge Management Strategy) Strategic

Setting the knowledge management strategy for the entire technical operations function, ensuring documentation is discoverable, accurate, and actively maintained by all teams.

Tableau Server / Power BI Premium (Executive Reporting) Advanced

Building and presenting executive-level dashboards on SLAs, incident trends, operational costs, and team performance to leadership and the board. You'll tell the story of our operational health.

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
Operational Strategy & Roadmap N/A N/A Contributes to strategy, proposes initiatives.
Operational Strategy & Roadmap N/A N/A Contributes to strategy, proposes initiatives.
Budget Allocation (Departmental) N/A N/A Recommends budget for specific workstreams up to £50K.
Vendor Selection (Tooling) N/A N/A Evaluates tools, provides technical recommendations.
Hiring & Firing (Team) N/A N/A Interviews candidates, provides feedback for junior roles.
Major Incident Response (P1/P2) N/A N/A Leads technical investigation, makes tactical decisions.
Organisational Design (Departmental) N/A N/A N/A
M&A Due Diligence (Operational Aspects) N/A N/A N/A

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.

Operational Expense Reduction
Reducing the running costs associated with ML infrastructure and support, including cloud spend, tooling, and overtime.
Target · Achieve a 10-15% year-on-year reduction in operational costs for the ML portfolio.

Identifying and implementing a strategy that reduces our AWS ML inference costs by £2M annually, or optimising our monitoring tool licenses to save £500K a year without compromising coverage.

Mean Time to Recovery (MTTR) for P1/P2 Incidents
The average time it takes to restore service after a critical or major incident impacting ML models.
Target · Maintain MTTR for P1 incidents below 30 minutes and P2 incidents below 60 minutes across all critical ML services.

After a major model outage, the team restores full service in 25 minutes, beating the 30-minute target, indicating effective incident management and automation.

ML System Uptime & SLA Attainment
Ensuring our critical machine learning models and the infrastructure they run on are available and performing within agreed service level agreements.
Target · Achieve 99.99% uptime for Tier 1 ML services and 99.9% for Tier 2 services, meeting all defined SLAs.

All customer-facing recommendation engines maintained 99.99% availability for the entire year, directly contributing to consistent user experience and revenue.

Team Engagement & Retention
Measuring the satisfaction and stability of your direct reports and the broader ML operations team.
Target · Maintain an employee engagement score above 85% and regrettable attrition below 10% annually.

The team's latest engagement survey shows an 88% satisfaction rate, and we've retained 95% of our top performers, indicating a healthy and thriving team culture.

Strategic Project Delivery Rate
The successful delivery of key initiatives aimed at improving ML operational excellence, such as new tooling rollouts, automation programmes, or architectural upgrades.
Target · Deliver 90% of all strategic roadmap projects on time and within budget.

Successfully rolling out a new enterprise-wide ML monitoring platform across 15 different model teams, completing the project 2 weeks ahead of schedule and under budget.

Strategic Influence & Thought Leadership
Your ability to shape the broader technical operations strategy and represent the company as an expert in ML operations.
  • You're regularly invited to present to the C-suite on operational strategy. Your insights are sought out for major architectural decisions. You might speak at industry conferences or publish articles on ML operational best practices. Other departments actively seek your input on their operational challenges, not just ML-related ones.
Organisational Resilience & Risk Management
Proactively identifying, mitigating, and communicating operational risks related to ML systems.
  • You've established a robust operational risk register for ML, complete with clear mitigation plans. You regularly conduct 'game days' or chaos engineering exercises to test system resilience. You present clear, actionable risk assessments to the board, showing a proactive stance rather than just reacting to failures.
Talent Development & Succession Planning
Building a strong, diverse, and capable team, and ensuring there's a clear pipeline for future leadership.
  • You have a clear development plan for each of your direct reports, with visible progression paths. You've successfully promoted multiple individuals into leadership roles within your function. Your team is recognised internally as a source of high-potential talent for other areas of the business.
Cross-Functional Collaboration & Alignment
Effectively working with other departments (Data Science, ML Engineering, Product, Security) to achieve shared goals and resolve conflicts.
  • You're seen as a trusted partner by other department heads. You've successfully mediated disagreements between teams on operational priorities. Your initiatives often involve successful collaboration across multiple functions, leading to integrated solutions rather than siloed efforts. People actually want to work with your teams.

7Would you like it

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

What people enjoy
Building and Scaling High-Performing Teams

You'll spend a good chunk of your week coaching your managers, reviewing their team's performance, and strategising on hiring and talent development. You'll get a real buzz from seeing your people grow and take on bigger challenges.

Successfully mentoring a Lead Analyst to become a competent Manager, or seeing your team's engagement scores rise after implementing a new development programme.

Driving Strategic Impact at an Enterprise Level

You'll be working on initiatives that affect the entire company, presenting to the board, and shaping our long-term technical strategy. Your decisions will have a direct, measurable impact on our revenue and operational efficiency.

Leading the initiative to migrate all critical ML services to a new, more resilient cloud architecture, resulting in a 20% reduction in P1 incidents and significant cost savings.

Solving Complex, Systemic Operational Challenges

You'll be tackling the 'hard problems' that no one else has managed to crack – recurring outages, scaling bottlenecks, or fundamental architectural weaknesses. This role is about finding and fixing the root causes of our biggest operational headaches.

Designing and implementing a new data validation framework that eliminates a class of data-related model failures that previously caused weekly incidents.

What frustrates people
  • Organisational inertia or resistance to necessary changes, especially when it involves other departments.
  • Budget constraints that prevent investment in critical operational tooling or additional headcount.
  • The constant balancing act between innovation speed and operational stability—Product always wants faster, you need safer.
  • Dealing with legacy systems or technical debt that limits your ability to implement modern operational practices.
  • Recruiting and retaining top-tier ML operations talent in a competitive market.
What this role does not give you
  • A purely individual contributor (IC) path focused solely on deep technical work.
  • A predictable, routine work schedule—incidents don't care about your calendar.
  • Freedom from organisational politics or the need to build consensus.
  • A role where you're not ultimately accountable for the operational performance of a large, complex system.

8Who you work with

This role directly drives the operational resilience and efficiency of our core machine learning products, which in turn impacts customer satisfaction, revenue generation, and our ability to scale. You're responsible for ensuring our ML systems are not just innovative, but also dependable and cost-effective, protecting brand reputation and enabling business growth.

Inside the business
  • VP of Technical Operations (your boss)
  • Chief Technology Officer (CTO)
  • Head of Data Science
  • Head of ML Engineering
  • Product Leadership (VPs and Directors)
  • Sales and Marketing Directors (they care about customer impact)
Outside the business
  • Key technology vendors (e.g., Datadog, AWS, Splunk)
  • Industry bodies and forums (for best practices and compliance)
  • External auditors (for operational controls and resilience)

9What you need before you start

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

  • Proven track record of leading and scaling technical operations teams (20+ people) for at least 5 years, ideally in an ML or data-intensive environment.
  • Extensive experience (10+ years) in Site Reliability Engineering, MLOps, or advanced technical support roles, with a strong understanding of production systems.
  • Demonstrated ability to define and execute strategic operational roadmaps that deliver measurable business impact.
  • Significant experience managing large operational budgets (£2M+ annually) and optimising costs.
  • Deep familiarity with cloud-native architectures (AWS or GCP preferred) and their operational best practices.
  • Excellent executive presence and communication skills, with a history of presenting to C-suite and Board-level stakeholders.

10What to practise next

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

MLOps Maturity Model Implementation

Moving beyond ad-hoc ML deployments, organisations are adopting structured MLOps maturity models (e.g., Google's MLOps maturity levels) to standardise and scale their ML operations. You'll need to drive this transformation.

Continuous Integration/Continuous Delivery (CI/CD) · Automated model testing and validation · Feature stores and data versioning · Model registry and metadata management · Infrastructure as Code (IaC) for ML environments

  • This quarter: Evaluate our current MLOps maturity against an industry standard framework.
  • Next 6 months: Develop a multi-year roadmap to advance our MLOps capabilities, focusing on automation and standardisation.
  • Next 12 months: Sponsor and oversee the implementation of a new model registry and CI/CD pipeline for a critical ML service.
  • Annually: Present progress on MLOps maturity to the board, highlighting business impact.

Quick win: Identify one manual, repetitive task in your ML deployment process and push for its automation this month. Small wins build momentum.

Generative AI for Operational Automation

Large Language Models (LLMs) and other generative AI tools are rapidly changing how we can automate complex operational tasks, from incident root cause analysis to knowledge base generation. You'll need to understand how to harness these for your teams.

Prompt engineering for operational tasks (e.g., lo · Integrating LLMs with ITSM and monitoring tools · Building AI agents for autonomous troubleshooting · Data privacy and security considerations for using · Measuring the ROI of AI-driven operational automat

  • This quarter: Pilot an LLM-powered tool for drafting incident summaries within your team.
  • Next 6 months: Research and evaluate vendors offering generative AI solutions for operational intelligence.
  • Next 12 months: Develop a strategy for secure and effective integration of generative AI into your department's workflows.
  • Annually: Present a business case for a significant investment in AI-driven operational automation.

Quick win: Encourage your managers to experiment with commercial LLMs (e.g., ChatGPT Enterprise, Claude) for drafting internal communications or summarising complex technical documents. Start small, learn fast.

11Staying current once you are in

What people here do to keep up
  • Actively participate in industry forums, conferences (e.g., O'Reilly MLOps, KubeCon, SREcon), and online communities dedicated to MLOps, SRE, and cloud operations.
  • Mentor junior leaders and technical staff, sharing your expertise and helping them grow their careers.
  • Contribute to open-source projects or publish articles/blog posts on ML operational best practices and challenges.
  • Pursue executive education programmes focused on technology leadership, digital transformation, or business strategy.
  • Engage in cross-functional leadership development programmes within the organisation to broaden your influence and understanding of other business areas.

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.

AI Governance & Responsible AI Operations

With increasing regulatory scrutiny (e.g., EU AI Act) and public awareness, ensuring ML models are fair, transparent, and ethically deployed is becoming a core operational responsibility. It's not just a legal team's problem anymore.

  • Model explainability (XAI) in production monitorin
  • Bias detection and mitigation in live systems
  • Operationalising AI ethics principles
  • Audit trails for model decisions
  • Compliance with emerging AI regulations

Advanced FinOps for ML

Cloud costs for ML inference and training can spiral quickly. As models scale and become more complex, optimising these costs without compromising performance or reliability is a strategic imperative. It's about getting the most bang for our buck.

  • Unit economics for ML inference (cost per predicti
  • Reserved instances and spot instance strategies fo
  • Cost allocation and chargeback models for ML infra
  • Automated cost anomaly detection
  • Optimising model serving infrastructure (e.g., GPU

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Enterprise Incident Management & Problem Management (ITIL v4 Strategic)
  • ML Model Governance & Lifecycle Management
  • Cloud Architecture & Cost Optimisation (AWS/GCP)
  • Site Reliability Engineering (SRE) Principles for ML
  • Data Lineage & Data Quality Management (Strategic)
  • Automation & Tooling Strategy

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

    Head of MLOps / Head of SRE

    You'd typically spend 3-5 years in a Head of MLOps or SRE role, managing multiple teams and owning the operational strategy for a specific domain.

    Skills to master

    • Scaling technical teams, defining and implementing SRE/MLOps frameworks, managing significant infrastructure budgets, cross-functional leadership, and executive communication.

    You're ready to move on when

    • Successfully led a major operational transformation programme (e.g., cloud migration, SRE adoption).
    • Consistently met or exceeded operational SLAs for critical systems under your purview.
    • Built and retained a high-performing team of 15+ engineers/managers.
    • Presented operational strategy and performance to senior leadership regularly.
  2. 2

    Senior Manager, Technical Operations (Large Organisation)

    Around 4-6 years as a Senior Manager in a large, complex technical operations environment, managing managers and multiple functions.

    Skills to master

    • Multi-team management, vendor management, large-scale incident command, budget oversight, and influencing product/engineering roadmaps for operational improvements.

    You're ready to move on when

    • Managed a budget of £1M+ and demonstrated cost-saving initiatives.
    • Successfully navigated complex organisational structures to deliver operational improvements.
    • Developed and mentored multiple team leads or managers.
    • Led the operational response for several major, company-impacting incidents.
  3. 3

    Principal / Staff Engineer (MLOps/SRE focus)

    A Principal or Staff Engineer with 12-15+ years of deep technical experience, who has also taken on significant leadership and architectural responsibilities without direct reports.

    Skills to master

    • Deep technical expertise in distributed systems and ML infrastructure, architectural design, technical strategy, cross-functional technical leadership, and driving adoption of best practices through influence.

    You're ready to move on when

    • Architected and delivered major operational platforms or systems.
    • Recognised as a company-wide expert in ML reliability or SRE.
    • Successfully mentored and guided multiple senior engineers.
    • Proven ability to influence technical direction at a strategic level without direct authority.

15Where this role leads

The long view: This work can lead to broader technology leadership, such as Chief Technology Officer or Chief Operating Officer.

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 Director of ML Operations is actually changing.

Where it leads

Roles this leads to

Also named as a next step, with no page in the catalogue yet:

  • Chief Technology Officer (CTO) / Chief Operating Officer (COO)

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 Data Science Foundations, worked against Director of ML Operations.

Data Science Foundations Level 7

Applied to your work in Director of ML Operations

1. To enable the learner to define the scope and landscape of Data Science and differentiate the roles of Data Scientists from other IT professionals. 2. To enable the learner to evaluate key topics within Data Science, including data administration, governance, and big data sources. 3. To enable the learner to describe the architecture and core elements of Apache Hadoop. 4. To enable the learner to analyse the advantages and disadvantages of utilising Artificial Intelligence techniques in a business context. 5. To enable the learner to critically analyse the impact of Big Data on digital transformation within organisations and its effects on users. 6. To enable the learner to review strategies for ensuring data compliance and explain the responsibilities and challenges faced by data specialists.

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.

See Your Progress Grow Illustration
Director of ML Operations
  • Enterprise Incident Management & Problem Management (ITIL v4 Strategic)
  • ML Model Governance & Lifecycle Management
  • Cloud Architecture & Cost Optimisation (AWS/GCP)
  • Site Reliability Engineering (SRE) Principles for ML
  • Data Lineage & Data Quality Management (Strategic)
  • Automation & Tooling Strategy
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

Director of ML Operations is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. VP of Technical Operations

    Typically 3-5 years as Director of ML Operations.

    This is the next natural step, expanding your scope to encompass all technical operations (e.g., infrastructure, security, network, as well as ML).

    • Enterprise infrastructure management and cloud strategy across all domains.
    • Advanced cyber security operations and compliance.
    • Global team management and distributed operations.
    • Strategic vendor management and contract negotiation at an enterprise scale.

  2. Chief Technology Officer (CTO) / Chief Operating Officer (COO)

    Roughly 5-10 years as Director/VP of Technical Operations.

    These roles represent the pinnacle of technical or operational leadership within a company.

    • Product development strategy and innovation.
    • Enterprise-wide digital transformation.
    • Regulatory compliance and industry advocacy.
    • Strategic partnerships and ecosystem development.
Working with AI on the job

Working with AI

Where AI is starting to help

As a Director of ML Operations, your time is gold. It's not about doing more manual tasks; it's about amplifying your strategic influence and empowering your teams. AI isn't here to replace you, but to give you superpowers, allowing you to focus on what truly matters: building a resilient, high-performing ML operations function.

Imagine having an intelligent assistant that handles the grunt work of operational oversight, risk assessment, and team communication. This isn't science fiction; it's what modern AI tools can do for you and your teams right now. You'll be using AI to gain deeper insights, automate routine strategic reporting, and even proactively identify potential operational risks before they escalate.

AI-Driven Operational Insights

Instead of manually sifting through dashboards or waiting for weekly reports, AI can provide real-time, executive-level summaries of ML model health, performance trends, and cost anomalies. It'll highlight the 'needle in the haystack' issues, allowing you to quickly grasp the strategic implications and direct your teams effectively.

Automated Strategic Risk Assessment

AI models can analyse incident data, system configurations, and architectural patterns to proactively identify emerging operational risks or single points of failure across your ML portfolio. You'll get early warnings about potential systemic issues, giving you time to implement preventative measures before they impact the business.

Intelligent Executive Communication Drafts

When a major incident occurs or a strategic update is due, AI can draft the initial versions of board reports, executive summaries, or stakeholder communications. It pulls data from various sources (Jira, monitoring tools, internal wikis), helping you craft clear, concise, and impactful messages in minutes, not hours.

Optimised Resource Allocation & Forecasting

AI can analyse historical operational data, incident patterns, and project roadmaps to forecast future resource needs (e.g., headcount, cloud capacity, tooling licenses). This helps you make data-driven decisions on budget allocation and team scaling, ensuring you're always one step ahead.

Common questions

Common questions

How do you become a Director of ML Operations?

Common routes in include Head of MLOps / Head of SRE (You'd typically spend 3-5 years in a Head of MLOps or SRE role, managing multiple teams and owning the operational strategy for a specific domain.), Senior Manager, Technical Operations (Large Organisation) (Around 4-6 years as a Senior Manager in a large, complex technical operations environment, managing managers and multiple functions.) and Principal / Staff Engineer (MLOps/SRE focus) (A Principal or Staff Engineer with 12-15+ years of deep technical experience, who has also taken on significant leadership and architectural responsibilities without direct reports.). Times vary with prior experience.

Where can a Director of ML Operations progress to?

This role can lead on to VP of Technical Operations (Typically 3-5 years as Director of ML Operations.) and Chief Technology Officer (CTO) / Chief Operating Officer (COO) (Roughly 5-10 years as Director/VP of Technical Operations.), depending on the skills you build.

What new skills matter most for a Director of ML Operations?

Increasingly, AI Governance & Responsible AI Operations and Advanced FinOps for ML. These are the skills our record of this role lists as growing.

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17Where to go from here

Other roles at Level 7

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 built in this role transfer across industries with significant data and machine learning investments, such as FinTech, HealthTech, e-commerce and SaaS. Building ML systems that are resilient, scalable and cost effective is needed across modern tech.

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 free Automation Fluency module 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.