UK

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

Senior AI Solutions Specialist

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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One email: what AI is changing in this job, and where to start free. With a link back here. Nothing else follows unless you confirm your address.

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

Also advertised as Senior Machine Learning Engineer · Applied AI Scientist · AI/ML Solutions Architect

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

NCC Education Level 5 Extended Diploma in Computing with Data Science NCC Education Limited · Level 5 · covers 4 of 7 standards
Pearson BTEC Level 5 Higher National Diploma in Computing for England Pearson Education Ltd · Level 5 · covers 4 of 7 standards
OCNLR Level 5 Diploma in Software Engineering with Artificial Intelligence OCN London · Level 5 · covers 2 of 7 standards
Pearson BTEC Level 5 Higher National Diploma in Cloud Computing Pearson Education Ltd · Level 5 · covers 2 of 7 standards
ATHE Level 5 Extended Diploma in Computing ATHE Ltd · Level 5 · covers 2 of 7 standards
Qualifi Level 5 Extended Diploma in Computer Science Qualifi Ltd · Level 5 · covers 1 of 7 standards

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

The units you would start with

Artificial Intelligence NCC Education Level 5 Extended Diploma in Computing with Data Science
Machine Learning Pearson BTEC Level 5 Higher National Diploma in Computing for England
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
Introduction to Artificial Intelligence Qualifi Level 5 Extended Diploma in Computer 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
Systems Fluency seeing the whole picture
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Your Plan Illustration

Built for Senior AI Solutions Specialist

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

  1. Artificial Intelligence NCC Education Limited · covers 4 of 7 standards Level 5
  2. Machine Learning Pearson Education Ltd · covers 4 of 7 standards Level 5
  3. Machine Learning Algorithms OCN London · covers 2 of 7 standards Level 5
  4. Data Analytics and Machine Learning ATHE Ltd · covers 2 of 7 standards Level 5
  5. Introduction to Artificial Intelligence Qualifi Ltd · covers 1 of 7 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 Senior AI Solutions Specialist?

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
  • Responsible AI & Ethics in Practice
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 AI Solutions Specialist 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

You take a fuzzy business problem and turn it into a working AI solution. You lead the technical design and delivery for significant client projects, bridging what's possible with AI and what the business needs. The work is about building solutions that get used and deliver real value.

4What you'd actually use

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

Cloud ML Platforms (AWS SageMaker, GCP Vertex AI, Azure ML Studio) Advanced

Architecting and implementing custom training/inference pipelines using SDKs. You'll manage endpoints, monitor for drift, and optimise costs on our chosen cloud platform (usually AWS or GCP).

Python ML Libraries (pandas, scikit-learn, TensorFlow/PyTorch, Hugging Face Transformers) Advanced

Deep expertise in building custom neural network architectures with TensorFlow or PyTorch. You'll also be using pandas for complex data manipulation and scikit-learn for classical ML, plus fine-tuning models from Hugging Face for various tasks.

Data Platforms (Snowflake, Databricks, Spark) Advanced

Designing efficient data processing jobs, optimising complex SQL queries, and working with Spark DataFrames. You'll understand data warehousing concepts like partitioning and clustering to ensure our models have good data.

Containerisation & Infra (Docker, Kubernetes, GitHub Actions/Jenkins) Expert

Writing complex Dockerfiles and Kubernetes manifests (Deployments, Services) to package and deploy our models. You'll use CI/CD principles with GitHub Actions or Jenkins to automate deployments and ensure reliability.

Demo & Visualisation Tools (Streamlit, Gradio, Power BI/Tableau) Advanced

Creating polished, client-ready demos using Streamlit or Gradio. You'll embed complex visualisations and explain model outputs effectively (e.g., using SHAP plots) to business audiences. You might also use Power BI or Tableau for dashboards.

Collaboration Suite (Jira, Confluence) Advanced

Configuring Jira workflows and Confluence spaces for new projects. You'll champion documentation best practices and mentor others on how to keep our knowledge organised and accessible.

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 & Tool Selection Proposes options, requires approval from Senior or Lead. Chooses standard tools/approaches, escalates for novel solutions. Makes final technical decisions within workstream scope; consults Lead/Manager on major architectural shifts or new platform adoption.
Project Timeline & Scope Changes Identifies potential delays, escalates to Manager. Proposes minor adjustments, seeks Manager approval. Recommends and justifies changes to workstream timelines; requires consultation with Lead/Manager for overall project impact.
Client Communication & Expectation Setting Prepares updates for Manager to deliver. Communicates routine updates directly to clients, escalates complex issues. Leads technical discussions with clients, manages expectations around model limitations and delivery timelines. Handles difficult conversations directly.
Mentorship & Junior Team Guidance Receives guidance and feedback. Provides informal guidance, answers questions. Formally mentors 1-2 junior team members, provides structured feedback, conducts code reviews, and helps unblock technical challenges.

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.

Project Success Rate (PoC to Production Pilot)
The percentage of AI Proof of Concepts (PoCs) you lead that successfully transition into a production pilot phase with a client.
Target · >80%

If you lead 5 PoCs this quarter and 4 of them get approved for a production pilot, that's an 80% success rate. We're looking for solutions that actually get momentum.

Solution Performance in Production
Achieving and maintaining agreed-upon performance metrics (e.g., accuracy, precision, recall, inference latency) for deployed models.
Target · Meet or exceed 95% of agreed-upon KPIs

For a fraud detection model, if the target recall is 90% and your deployed model consistently hits 92%, you're doing well. If inference latency needs to be under 100ms and you're at 80ms, even better.

Time-to-Value for Client Solutions
Reducing the average time it takes to deliver a functional AI solution from initial concept to a demonstrable PoC or pilot.
Target · Reduce average delivery time by 15% year-on-year

If a typical PoC used to take 10 weeks, you're aiming to get that down to 8.5 weeks through better design, tooling, or process improvements.

Mentee Progression
The advancement of junior team members you formally mentor, measured by their ability to take on more complex tasks or achieve promotion.
Target · At least one mentored L2 promoted to L3 within 18 months

You've been mentoring Sarah for 15 months, and she's now confidently leading her own smaller workstreams and is being considered for a Senior Specialist role next quarter. That's a win.

Technical Leadership & Architectural Soundness
Your ability to design robust, scalable, and maintainable AI solution architectures, and to guide other team members in implementing them correctly.
  • You're consistently asked to review architectural designs for new projects. Your proposed solutions are rarely challenged on fundamental technical grounds by Leads or Managers. Junior team members come to you for technical guidance before escalating to their manager. Your solutions stand up well to production challenges.
Client & Stakeholder Trust
Being seen as a credible, reliable technical expert by clients and internal business partners, who proactively seek your input on new AI opportunities or challenges.
  • Clients specifically request you for follow-on work or new projects. You're invited to early-stage discussions about potential AI use cases. Business stakeholders trust your estimates and technical assessments. They'll often say things like, 'Let's run this by [Your Name] first, they usually spot the tricky bits.'
Problem Translation & Pragmatism
Your skill in taking vague business problems, translating them into well-defined machine learning challenges, and then proposing pragmatic, value-driven solutions.
  • You consistently deliver solutions that directly address the client's core problem, even if it's not the 'sexiest' AI approach. You can clearly articulate trade-offs between different technical approaches in business terms. You're known for avoiding 'science projects' and focusing on deployable impact.

7Would you like it

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

What people enjoy
Solving Hard, Real-World Problems

You're energised by tackling complex technical challenges that have a direct business impact. You enjoy the puzzle of translating messy data and vague requirements into a functional, elegant solution.

You spend a few hours wrestling with a tricky data integration, but the satisfaction of seeing the model train successfully on clean data makes it worthwhile.

Seeing Your Work Make a Tangible Impact

It's not enough for you to just build models; you want to see them deployed, used, and making a difference. You're driven by the feedback loop of production performance and client satisfaction.

Getting an email from a client saying your forecasting model saved them £50K in inventory costs is what truly makes your day.

Continuous Learning and Growth

The fast pace of AI excites you. You're constantly experimenting with new techniques, reading research papers, and keen to apply the latest advancements to client problems. You love sharing what you've learned with others.

You spent your lunch break experimenting with a new prompt engineering technique you read about, and then immediately think about how it could improve a current project.

What frustrates people
  • The Data Chase: You'll spend a significant chunk of your time—probably 60-70%—cleaning, labelling, and begging for access to messy, siloed data. Only then can you get to the actual 'AI' part of the job. It's not glamorous, but it's essential.
  • Managing Magical Expectations: You'll constantly have to explain to stakeholders that AI is advanced maths, not magic. You can't build a perfect sentient oracle with last quarter's patchy sales data, no matter how much they wish you could.
  • The Sales-Engineering Gap: Sometimes, the sales team promises a client a solution that is technically impossible or would require a year of R&D. You're often the one who has to deliver the bad news, or figure out a pragmatic alternative.
  • 'Just use ChatGPT for that': This is the new default suggestion from every non-technical person for every single problem, regardless of data privacy, cost, or technical fit. You'll need patience and clear explanations.
  • The Last Mile Problem: Your model might achieve 98% accuracy in the lab, but integrating it with the client's legacy mainframe system from 1998 can often be the real project killer. The real world isn't always shiny and new.
  • Explaining Confidence Intervals: The soul-crushing experience of explaining to an executive why a prediction is a probability distribution, not a single point of certainty, for the fifth time. Yes, it's that important, and yes, you'll do it again.
What this role does not give you
  • A perfectly clean, well-structured dataset for every project.
  • Guaranteed deployment of every Proof of Concept you build.
  • A quiet, uninterrupted environment for deep work every single day (expect meetings and ad-hoc requests).
  • A role where you only focus on pure research or theoretical model development without practical application.

8Who you work with

This role directly influences the success of our client AI projects, ensuring that solutions are technically sound, meet business needs, and actually get deployed. Your work builds our reputation and directly contributes to client retention and new business growth by proving the value of our AI capabilities.

Inside the business
  • AI Solutions Leads and Managers
  • Technical Architects
  • Data Engineers
  • Product Managers for internal tools
  • Sales and Account Management teams
Outside the business
  • Senior client technical teams
  • Client business stakeholders (e.g., Head of Operations, Marketing Director)
  • Third-party vendors and partners

9What you need before you start

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

  • A proven track record of successfully delivering at least 3-5 end-to-end AI/ML projects from concept to production, ideally in a client-facing or product development setting.
  • Demonstrable experience in designing and implementing robust MLOps pipelines, including CI/CD for ML models.
  • Strong software engineering fundamentals, including clean code principles, version control (Git), and testing methodologies.
  • Experience mentoring junior team members or leading small technical workstreams.
  • The ability to clearly articulate complex technical concepts to both technical and non-technical audiences, with examples.

10What to practise next

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

Advanced MLOps & Production Engineering

As our solutions become more complex and critical, the ability to build and maintain highly resilient, scalable, and cost-efficient production ML systems will be paramount. This means moving beyond basic CI/CD to full-blown MLOps platforms.

Model Observability & Monitoring · Automated Model Retraining & Versioning · Cost Optimisation for Cloud ML Workloads · A/B Testing & Canary Deployments for Models

  • This quarter: Take ownership of the monitoring setup for a deployed model, ensuring all key metrics are tracked and alerted.
  • Next quarter: Design and implement an automated retraining pipeline for an existing production model.
  • Within 6 months: Research and propose a cost optimisation strategy for one of our larger cloud ML workloads.
  • Within 9 months: Lead a discussion on best practices for A/B testing new model versions in production.

Quick win: Review the current monitoring dashboards for your projects. Are there any gaps? What critical metric isn't being tracked that should be?

11Staying current once you are in

What people here do to keep up
  • Actively participating in online ML communities (e.g., Kaggle, Hugging Face forums, relevant Slack channels) to stay current and share knowledge.
  • Attending industry conferences or meetups (e.g., NeurIPS, KDD, local AI meetups) to network and learn about new trends.
  • Contributing to open-source AI projects or maintaining a personal portfolio of ML projects on GitHub to showcase your practical skills.
  • Regularly reading leading AI research papers and blogs (e.g., ArXiv, Google AI Blog, OpenAI Blog) to keep your theoretical knowledge sharp.
  • Taking advanced online courses or specialisations in specific areas like Deep Learning, Reinforcement Learning, or MLOps from platforms like Coursera, Udacity, or edX.

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

Critical within 6 months—this is already happening, not just a future thing. Competitors are using large language models (LLMs) to draft reports in 10 minutes that used to take two hours. Analysts who figure this out will outproduce their peers significantly. It's a game-changer for productivity and new solution types.

  • Context windows and token limits
  • Temperature settings for different tasks
  • RAG (Retrieval-Augmented Generation) architectures
  • Output validation and hallucination detection
  • Prompt chaining for complex analysis

Responsible AI & Ethics in Practice

Important within 12 months. As AI becomes more pervasive, the focus on fairness, transparency, and accountability is growing rapidly. Regulators are getting serious, and clients are asking tough questions about bias and explainability. Ignoring this isn't an option anymore.

  • Bias detection and mitigation techniques
  • Explainable AI (XAI) methods
  • Fairness metrics and their application
  • Data lineage and auditing for AI
  • Privacy-preserving AI techniques

13What you’ll use

What you’ll use

Skills this role draws on

Technical

  • Solution Architecture
  • MLOps (Machine Learning Operations)
  • Proof of Concept (PoC) to Production
  • Business-to-Technical Translation
  • Model Evaluation & Selection
  • Use Case Discovery & Prioritization

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

    From AI Solutions Specialist (L2)

    2-3 years at L2

    Skills to master

    • Mastering end-to-end project ownership, demonstrating strong client communication, and consistently delivering production-ready PoCs. You'll also need to start providing informal technical guidance to new joiners.

    You're ready to move on when

    • Consistently delivering complex PoCs that move to pilot phase.
    • Proactively identifying and solving technical challenges without constant supervision.
    • Receiving positive feedback from clients on your technical contributions and communication.
    • Starting to mentor junior team members informally, helping them with code reviews or unblocking issues.
  2. 2

    Experienced Software Engineer (with ML focus)

    5-7 years in software engineering, with 2-3 years focused on ML systems

    Skills to master

    • Transitioning from pure software development to the nuances of ML model lifecycle (e.g., MLOps, model drift, evaluation metrics). You'll need to develop strong client-facing communication skills and the ability to translate business problems into ML solutions.

    You're ready to move on when

    • Successfully built and deployed several ML-powered features or services in a production environment.
    • Deep understanding of software architecture principles and how they apply to ML systems.
    • Demonstrated ability to learn and apply new ML frameworks and techniques quickly.
    • Strong desire to work directly with clients and solve business problems using AI.
  3. 3

    Data Scientist (with strong engineering skills)

    5-7 years as a Data Scientist, with 2-3 years focused on productionising models

    Skills to master

    • Moving beyond experimental model building to robust, scalable production systems. This means deepening your MLOps knowledge, software engineering best practices, and focusing on the end-to-end solution rather than just the model itself. You'll also need to refine your client-facing skills.

    You're ready to move on when

    • Proven ability to take models from research to production, handling deployment and monitoring.
    • Strong programming skills (Python) and familiarity with software engineering principles.
    • Desire to focus more on the 'how' of solution delivery and less on pure research.
    • Comfortable presenting complex analytical findings and technical designs to diverse audiences.

15Where this role leads

The long view: This work can lead into director or VP roles shaping AI strategy across a business, or into a chief AI officer role setting AI direction at board level.

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 AI Solutions Specialist is actually changing.

Where it leads

Roles this leads to

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

  • Principal AI Solutions Specialist (L5 - Individual Contributor Path)

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 Artificial Intelligence, worked against Senior AI Solutions Specialist.

Artificial Intelligence Level 5

Applied to your work in Senior AI Solutions Specialist

This unit aims to provide learners with an understanding of Artificial Intelligence (AI) and its applications, enabling them to apply AI search strategies and knowledge representation techniques to solve problems. Learners will also assess techniques for reasoning with uncertain knowledge and understand machine learning techniques, demonstrating a comprehensive knowledge of AI principles and applications.

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 AI Solutions Specialist plan, free
See Your Progress Grow Illustration
Senior AI Solutions Specialist
  • Solution Architecture
  • MLOps (Machine Learning Operations)
  • Proof of Concept (PoC) to Production
  • Business-to-Technical Translation
  • Model Evaluation & Selection
  • Use Case Discovery & Prioritization
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 AI Solutions Specialist is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Lead AI Solutions Specialist (L4)

    3-5 years as a Senior Specialist

    This is a significant step up, moving from leading workstreams to architecting multi-system solutions and potentially leading a small team of specialists. You'll become the primary technical authority for larger, more complex client engagements.

    • Enterprise Solution Architecture Designing AI solutions that integrate across multiple client systems and departments.
    • Vendor Management Evaluating and managing relationships with third-party technology providers.
    • Technical Due Diligence Assessing the technical feasibility and risks of new business opportunities.
    • IP Development Identifying opportunities to build reusable components or frameworks that accelerate future projects.

  2. Principal AI Solutions Specialist (L5 - Individual Contributor Path)

    4-6 years as a Senior Specialist

    This is a highly respected individual contributor path, focusing on solving the most complex technical problems, developing reusable intellectual property (IP), and acting as an internal and external thought leader. You'll be the 'deep expert' everyone turns to.

    • Advanced AI Research & Application Deep expertise in niche AI areas (e.g., advanced NLP, computer vision, reinforcement learning) and applying them to unique challenges.
    • Complex System Optimisation Solving performance, scalability, and cost challenges in highly distributed AI systems.
    • IP Development & Productisation Creating reusable AI components, frameworks, or accelerators that can be productised.
    • Technical Mentorship at Scale Mentoring multiple Senior and Lead Specialists, shaping the technical capabilities of the wider team.
Working with AI on the job

Working with AI

Where AI is starting to help

Let's be real, the 'AI' part of building AI solutions often gets bogged down in repetitive tasks. But what if you could offload the grunt work to AI itself? Imagine spending less time on boilerplate code or tedious documentation, and more time on the truly interesting, impactful parts of your job.

As a Senior AI Solutions Specialist, your brainpower is best spent on complex problem-solving, architectural design, and client engagement. We use a suite of AI-powered tools to automate the mundane, giving you back precious hours every week. This isn't just about efficiency; it's about making your job more enjoyable and impactful.

Automated Code Scaffolding

Use AI code assistants like GitHub Copilot to auto-generate boilerplate code for data ingestion, exploratory data analysis (EDA), and standard model training pipelines. It's like having a super-fast junior developer at your fingertips for the repetitive stuff.

Accelerated Model Benchmarking

Leverage AutoML tools (think Google's Vertex AI AutoML or H2O.ai) to automatically train and evaluate dozens of different model architectures on a new dataset. This quickly identifies the top 3-5 candidates for deeper exploration, saving you days of manual experimentation.

Instant Research Synthesis

Use LLM-powered research tools to quickly summarise the latest academic papers on a specific technique (e.g., 'Summarise the top 3 new approaches to anomaly detection in time-series data'). Or, use them to debug obscure error messages by instantly searching across forums and documentation. No more endless scrolling.

Draft-Zero Documentation & Decks

After you've finished a Jupyter Notebook analysis, use an AI agent to parse your code and comments. It can then generate a first draft of the technical documentation for Confluence and even a 10-slide PowerPoint deck explaining the methodology and results for a business audience. It's a massive head start.

Common questions

Common questions

How do you become a Senior AI Solutions Specialist?

Common routes in include From AI Solutions Specialist (2-3 years at L2), Experienced Software Engineer (with ML focus) (5-7 years in software engineering, with 2-3 years focused on ML systems) and Data Scientist (with strong engineering skills) (5-7 years as a Data Scientist, with 2-3 years focused on productionising models). Times vary with prior experience.

Where can a Senior AI Solutions Specialist progress to?

This role can lead on to Lead AI Solutions Specialist (3-5 years as a Senior Specialist) and Principal AI Solutions Specialist (4-6 years as a Senior Specialist), depending on the skills you build.

What new skills matter most for a Senior AI Solutions Specialist?

Increasingly, Prompt Engineering & LLM Integration and Responsible AI & Ethics in Practice. These are the skills our record of this role lists as growing.

Free tools

Want a read on where you stand?

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

Read the Zavmo manifesto to understand how you will be taught Meet your tutors

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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 built as an AI Solutions Specialist transfer well. You could move into product management for AI-powered products, specialise in AI research, or move into venture capital with a focus on AI start-ups.

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 AI Solutions Specialist 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.