At a Glance
- Tasks: Lead the transformation of AI in higher education, creating impactful solutions for students and staff.
- Company: Global University Systems, pioneering AI in education.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Shape the future of AI in education and make a real difference.
- Qualifications: 6+ years in IT delivery with strong AI/ML experience.
- Other info: Join a dynamic team driving innovation across multiple universities.
The predicted salary is between 72000 - 108000 £ per year.
Global University Systems is building a modern AI platform to power how our universities recruit, teach, support, and serve students. The Director of AI will lead this transformation, turning promising ideas into production-grade AI solutions that create real value for students and staff. This role owns the AI roadmap end-to-end: from identifying high-impact use cases, to running rapid experiments, to scaling successful solutions across multiple Higher Education Institutions.
What you’ll do:
- Set vision and strategy
- Own and continually refine the AI initiatives portfolio and roadmap for the group.
- Run a structured intake process for new ideas, assessing value, feasibility, data readiness, ethical risk, and compliance.
- Define clear success measures for every proof of concept (PoC) and MVP, including KPIs, leading indicators, and decision gates to continue, pivot, or stop.
- Lead PoCs and MVPs
- Run time-boxed PoCs (2–8 weeks) and MVPs (8–16 weeks) using Agile methods (Scrum or Kanban) with regular demos and retrospectives.
- Build and lead cross-functional squads including Product Owner, Data Scientists, ML Engineers, Integration Engineers, Solution Architect, and domain experts from Admissions, Registry and Student Services.
- Design experiments, test hypotheses, and iterate quickly, ensuring human-in-the-loop for critical decisions such as admissions triage or student risk flagging.
- Embed Responsible AI practices from the start, including bias assessment, explainability, accessibility, documentation, and impact assessments tailored to higher education.
- Scale platforms and solutions
- Take validated MVPs into production with robust architecture, integration patterns, security controls, SLAs, and operational runbooks.
- Establish MLOps across the university environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies.
- Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as:
- Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support.
- Student Records & Registry: data quality checks, anomaly detection, and predictive alerts for progression and retention.
- Timetabling & Operations: demand forecasting, optimization support, and conflict detection.
- Teaching & Learning: content summarization, tutoring assistants, accessibility tools, and feedback synthesis respecting academic integrity.
- Student Support & Wellbeing: intelligent case routing, early warning signals, and proactive nudges designed with strong ethics and safeguarding.
- Research Administration: grant discovery, compliance support, and metadata enrichment.
- Coordinate integrations with core systems including SIS (e.g., Banner, Workday Student), LMS/VLE (Moodle, Canvas), CRM (Salesforce), library systems, HR/Finance (ERP), and identity platforms (e.g., Azure AD).
- Implement a practical AI governance framework that includes a use case register, risk scoring, impact assessments, model documentation, and audit trails.
- Ensure compliance with GDPR, accessibility standards (e.g., WCAG), information security policies, and emerging AI regulations.
- Define and enforce guardrails for data and GenAI use: data access controls, PII handling, prompt/data leakage prevention, and content moderation.
- Partner with Deans, Registry, Admissions, Student Services, IT, Legal and other stakeholders to align on priorities and rollout plans.
- Communicate progress through clear dashboards and storytelling, highlighting wins and lessons learned to build momentum.
- Prepare teams and students for AI adoption with training, standard operating procedures, ethical use guidelines, and tailored communications.
- Lead RFPs and SOWs for AI vendors, data providers, and implementation partners, including evaluation of cost, performance, and scalability.
- Track and optimize cloud and AI platform spend, balancing cost with value; negotiate campus-wide licensing where beneficial.
What you bring:
Education
- Bachelor’s or Master’s degree in Data Science, Computer Science, Business Analytics, or a related discipline.
Knowledge and skills
- Essential
- Product and outcome mindset: frames problems as hypotheses, focuses on measurable impact, and is willing to stop or pivot when evidence is weak.
- Delivery excellence: strong Agile practices, risk-based planning, and disciplined stage-gates from idea to production.
- Technical fluency: able to bridge data science, engineering, security, and business stakeholders, and make informed technical decisions.
- Responsible AI and data literacy: understands and embeds ethics, privacy, and accessibility by design.
- Change leadership: confident working across diverse academic and professional services communities to drive adoption.
- Desirable
- Experience with major cloud and AI stacks such as Azure OpenAI / OpenAI, Azure ML, Databricks, Kubernetes, and Docker.
- Familiarity with data platforms such as Azure Data Lake, Synapse/ADF, Delta Lake, and messaging platforms (e.g., event hubs, service bus).
- Exposure to MLOps and observability tools (e.g., MLflow/model registry, GitHub Actions, Prometheus/Grafana, Evidently AI).
- Knowledge of integrating with SIS (Banner/Workday Student), LMS (Moodle/Canvas), CRM (Salesforce), ERP (Oracle/Workday), and IdP (Azure AD).
- Comfort with collaboration and reporting tools such as Jira/Azure Boards, Confluence, and Power BI.
Experience
- Essential
- 6+ years in IT delivery, product, or program leadership roles, including at least 3 years leading AI/ML or advanced analytics initiatives end-to-end.
- Proven track record running Agile PoCs and MVPs and scaling them into production within complex organizations.
- Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices.
- Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow, Azure ML, Databricks, SageMaker or Vertex) plus observability practices.
- Familiarity with higher education processes and integrations across SIS, LMS, CRM, and ERP environments.
- Knowledge of data privacy, accessibility, security, and Responsible AI in academic or similarly regulated contexts.
- Excellent stakeholder communication skills with the ability to translate technical topics into clear outcomes for non-technical audiences.
- Desirable
- Experience designing or operating an AI platform or shared AI services used across multiple domains or business units.
- Hands-on work with vector databases and retrieval pipelines (e.g., Azure AI Search, Pinecone) and guardrails for GenAI.
- Background in enterprise architecture and integration patterns (event-driven, REST, GraphQL).
- Relevant certifications such as Agile/Scrum (PSM/CSM), Azure AI Engineer/DP-100, PMI-ACP/SAFe, or security/privacy certifications.
If you are excited by the opportunity to shape the future of AI in higher education and deliver impact at scale, we encourage you to apply and join us on this journey.
AI Director employer: Global University Systems
Contact Detail:
Global University Systems Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Director
✨Tip Number 1
Network like a pro! Reach out to people in your field on LinkedIn or at industry events. A friendly chat can lead to opportunities that aren’t even advertised yet.
✨Tip Number 2
Show off your skills! Create a portfolio or a personal project that highlights your AI expertise. This gives you something tangible to discuss during interviews and makes you stand out.
✨Tip Number 3
Prepare for interviews by researching the company’s current AI initiatives. Tailor your responses to show how your experience aligns with their goals, especially in higher education.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace AI Director
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight your experience with AI and leadership in tech. We want to see how your skills align with our mission to transform higher education through AI.
Showcase Your Achievements: Don’t just list your responsibilities; share specific examples of successful projects you've led, especially those involving AI or Agile methodologies. We love seeing measurable impacts!
Be Clear and Concise: Keep your application straightforward and to the point. Use clear language to describe your experience and avoid jargon unless it’s relevant. We appreciate clarity as much as you do!
Apply Through Our Website: We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity!
How to prepare for a job interview at Global University Systems
✨Know Your AI Stuff
Make sure you brush up on the latest trends and technologies in AI, especially those relevant to higher education. Be ready to discuss specific AI techniques like NLP or classification, and how they can be applied to improve student services.
✨Showcase Your Leadership Skills
As a Director of AI, you'll need to lead cross-functional teams. Prepare examples of how you've successfully managed diverse groups in the past, particularly in Agile environments. Highlight your experience with running PoCs and MVPs, and how you’ve driven projects from idea to production.
✨Understand the Ethical Side
Familiarise yourself with Responsible AI practices. Be prepared to discuss how you would implement ethical considerations in AI projects, such as bias assessment and compliance with GDPR. This shows that you’re not just technically savvy but also socially responsible.
✨Communicate Clearly
You’ll need to translate complex technical concepts into clear outcomes for non-technical stakeholders. Practice explaining your past projects in simple terms, focusing on the impact and value created. This will demonstrate your ability to drive adoption and change across the organisation.