Data & AI Consultant Apprenticeship

Entry-level Data & AI Consultant

Role Overview Entry-level Data & AI Consultant role for individuals looking to build a career in data engineering and AI. You will work on real client projects, supporting the design and delivery of data pipelines, models, dashboards, and AI-driven solutions. This is not a passive learning role. You will be expected to contribute quickly.

---

Wage

£15,600 for your first year, then could increase depending on your age
National Minimum Wage rate for apprentices
Minimum wage rates (opens in new tab)

---

Training Course

Artificial intelligence (AI) and automation practitioner (level 4)

---

Hours

Monday to Friday, 9.00am to 5.00pm.
37 hours 30 minutes a week

---

Start Date

Monday 17 August 2026

---

Duration

1 year 6 months

---

Positions Available

1

---

Job Responsibilities

Most of your apprenticeship is spent working. You’ll learn on the job by getting hands-on experience.

What you'll do at work

• Build and support data pipelines (ETL/ELT)
• Work with data across Bronze / Silver / Gold layers
• Transform and model data for reporting and analytics
• Support development of Power BI dashboards
• Assist in AI use cases (automation, simple models, insights)
• Work within Azure-based environments (Databricks, Data Lake, etc.)
• Test, validate, and document solutions

---

Location

Unit 1
160 West End Lane
London
NW6 1HU

---

Training Provider

LEARNING FOR FUTURES LTD

---

Training Course

Artificial intelligence (AI) and automation practitioner (level 4)

Understanding apprenticeship levels (opens in new tab)

---

What you'll learn

Course contents

• Review, establish, follow and or amend policies and procedures on data and information security.
• Follow ethical, responsible and safe working practices respecting confidentiality and sensitive organisational matters.
• Undertake analysis to identify if automation is viable. Including assessing risks such as data quality, process maturity and unintended consequences of AI automation projects, such as the impact on job roles.
• Engage with non-technical staff to understand their roles, responsibilities, and concerns when automation solutions are proposed and implemented. Adapt approach to support workforce needs when implementing solutions that impacts the workforce.
• Support with the introduction, adaption, and implementation of change. Contribute to constructive dialogue between leaders and employees about the adoption of AI and automation solutions.
• Review and complete workflow and process mapping to identify problems or inefficiencies and recommend solutions including pilots, incremental changes and scaling opportunities.
• Use automation design tools to suit the organisational context to configure, adapt and implement AI or automation solutions, such as conversational agents, text processing AI, workflow automation platforms and cloud based SaaS or PaaS.
• Create and refine prompts for AI tools, using iterative testing to achieve accurate and useful outputs.
• Apply analytical and computational techniques using tools and datasets to design, evaluate, and optimise automation solutions.
• Integrate AI and automation technologies to collect, process, and manage data effectively, enabling intelligent and efficient system operation.
• Design, integrate, and test digital workflows and AI automation tools using APIs, connectors, or low-or no-code integration methods.
• Iterate solutions based on testing and feedback to ensure reliability, security, accessibility, and alignment with organisational needs.
• Identify opportunities to deliver automation. Support leaders in integrating ethical, empathetic approaches when decision-making.
• Support in the identification and evaluation of opportunities for increased productivity. For example, use of low-or no-code tools, streamlining processes and use of AI platforms.
• Make evidence based suggestions to support governance, outcomes and facilitate improvement for example cost benefit analysis.
• Report on productivity and efficiency savings and the opportunities for automation and where applicable when automation does not improve experience or processes.
• Contribute to sustainable and efficient AI and automation solutions.
• Support with the delivery of training to technical and non-technical user groups or audiences adapting content and format responding to feedback and organisational context.
• Contribute to the creation and or adaption of resources such as user guides, training materials, process documents to meet user requirements.
• Work collaboratively to deploy AI and automation strategies. Support where required to deal with the impact of automation for example retraining, redeployment, or upskilling of affected staff.
• Undertake data analysis, preparation, and conversion to support automation solutions.
• Present and communicate information including the translation of technical concepts into accessible materials to support clear dialogue with stakeholders.
• Work with others to achieve agreed outcomes or outputs. Provide evidence-based analysis and insight to leaders on the likely human impacts of automation projects.
• Use project management principles, techniques and tools to support the development of clear, balanced communications and briefings, articulating both opportunities and risks.
• Keep up to date with existing, evolving, emerging technologies and sector trends in AI, automation and technology including methods to evaluate vendor and supplier solutions.
• Apply ethical and human-centred design principles when scoping, developing, and deploying automation and AI solutions, underpinned by robust governance.
• Apply technical understanding to help align business needs with technical capabilities, supporting the development of solutions that are scalable, efficient, and aligned with the organisation’s strategic objectives.
• Undertake assurance activities to evidence responsible AI and automation, including maintaining clear documentation of design and decision-making, contributing to risk assessments, and applying assurance frameworks to support compliance with organisational, regulatory, and ethical standards.
• Apply algorithmic impact assessment and workforce equality monitoring techniques when scoping, implementing, and reviewing AI and automation projects. Gather and analyse relevant workforce data, identify potential equality risks, and contribute evidence-based recommendations to support fair and inclusive adoption.
• Review, establish, follow and or amend policies and procedures on data and information security.
• Follow ethical, responsible and safe working practices respecting confidentiality and sensitive organisational matters.
• Undertake analysis to identify if automation is viable. Including assessing risks such as data quality, process maturity and unintended consequences of AI automation projects, such as the impact on job roles.
• Engage with non-technical staff to understand their roles, responsibilities, and concerns when automation solutions are proposed and implemented. Adapt approach to support workforce needs when implementing solutions that impacts the workforce.
• Support with the introduction, adaption, and implementation of change. Contribute to constructive dialogue between leaders and employees about the adoption of AI and automation solutions.
• Review and complete workflow and process mapping to identify problems or inefficiencies and recommend solutions including pilots, incremental changes and scaling opportunities.
• Use automation design tools to suit the organisational context to configure, adapt and implement AI or automation solutions, such as conversational agents, text processing AI, workflow automation platforms and cloud based SaaS or PaaS.
• Create and refine prompts for AI tools, using iterative testing to achieve accurate and useful outputs.
• Apply analytical and computational techniques using tools and datasets to design, evaluate, and optimise automation solutions.
• Integrate AI and automation technologies to collect, process, and manage data effectively, enabling intelligent and efficient system operation.
• Design, integrate, and test digital workflows and AI automation tools using APIs, connectors, or low-or no-code integration methods.
• Iterate solutions based on testing and feedback to ensure reliability, security, accessibility, and alignment with organisational needs.
• Identify opportunities to deliver automation. Support leaders in integrating ethical, empathetic approaches when decision-making.
• Support in the identification and evaluation of opportunities for increased productivity. For example, use of low-or no-code tools, streamlining processes and use of AI platforms.
• Make evidence based suggestions to support governance, outcomes and facilitate improvement for example cost benefit analysis.
• Report on productivity and efficiency savings and the opportunities for automation and where applicable when automation does not improve experience or processes.
• Contribute to sustainable and efficient AI and automation solutions.
• Support with the delivery of training to technical and non-technical user groups or audiences adapting content and format responding to feedback and organisational context.
• Contribute to the creation and or adaption of resources such as user guides, training materials, process documents to meet user requirements.
• Work collaboratively to deploy AI and automation strategies. Support where required to deal with the impact of automation for example retraining, redeployment, or upskilling of affected staff.
• Undertake data analysis, preparation, and conversion to support automation solutions.
• Present and communicate information including the translation of technical concepts into accessible materials to support clear dialogue with stakeholders.
• Work with others to achieve agreed outcomes or outputs. Provide evidence-based analysis and insight to leaders on the likely human impacts of automation projects.
• Use project management principles, techniques and tools to support the development of clear, balanced communications and briefings, articulating both opportunities and risks.
• Keep up to date with existing, evolving, emerging technologies and sector trends in AI, automation and technology including methods to evaluate vendor and supplier solutions.
• Apply ethical and human-centred design principles when scoping, developing, and deploying automation and AI solutions, underpinned by robust governance.
• Apply technical understanding to help align business needs with technical capabilities, supporting the development of solutions that are scalable, efficient, and aligned with the organisation’s strategic objectives.
• Undertake assurance activities to evidence responsible AI and automation, including maintaining clear documentation of design and decision-making, contributing to risk assessments, and applying assurance frameworks to support compliance with organisational, regulatory, and ethical standards.
• Apply algorithmic impact assessment and workforce equality monitoring techniques when scoping, implementing, and reviewing AI and automation projects. Gather and analyse relevant workforce data, identify potential equality risks, and contribute evidence-based recommendations to support fair and inclusive adoption.