At a Glance
- Tasks: Lead the transformation of AI in higher education, creating impactful solutions for students and staff.
- Company: Join Global University Systems, a pioneer in modernising education through AI.
- 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 and leadership skills.
- Other info: Collaborative environment with a focus on ethical AI practices and innovation.
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, Student Records & Registry, Timetabling & Operations, Teaching & Learning, Student Support & Wellbeing, and Research Administration. 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).
- Govern AI responsibly: 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.
- Drive adoption and change: 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.
- Manage vendors and financials: 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: Product and outcome mindset; delivery excellence; technical fluency; responsible AI and data literacy; change leadership; 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; exposure to MLOps and observability tools; knowledge of integrating with SIS, LMS, CRM, ERP, and IdP; comfort with collaboration and reporting tools.
- Experience: 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, data pipelines, APIs, and microservices. Hands-on experience with cloud platforms and MLOps tools 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. 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 and guardrails for GenAI. Background in enterprise architecture and integration patterns. Relevant 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 in Wolverhampton employer: Top End jobs
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Top End jobs Recruiting Team
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We think this is how you could land AI Director in Wolverhampton
✨Tip Number 1
Network like a pro! Reach out to folks in your industry on LinkedIn or at events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills in real-time! Consider doing a live demo of your work or projects during interviews. It’s a great way to showcase your expertise and passion.
✨Tip Number 3
Prepare for those tricky questions! Think about how you’d tackle challenges in AI and have examples ready. We want to see your thought process and problem-solving skills.
✨Tip Number 4
Don’t forget to follow up! After an interview, drop a quick thank-you note. It shows you’re keen and keeps you fresh in their minds. Plus, it’s just good manners!
We think you need these skills to ace AI Director in Wolverhampton
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter to highlight how your experience aligns with the AI Director role. Use keywords from the job description to show that you understand what we're looking for.
Showcase Your Achievements: Don’t just list your responsibilities; focus on what you've achieved in your previous roles. Quantify your successes where possible, as this will help us see the real impact you've made in past positions.
Be Clear and Concise: Keep your application clear and to the point. We appreciate well-structured documents that are easy to read. Avoid jargon unless it’s relevant to the role, and make sure your passion for AI shines through!
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How to prepare for a job interview at Top End jobs
✨Know Your AI Stuff
Make sure you brush up on the latest AI trends and technologies relevant to higher education. Be ready to discuss specific AI techniques, like NLP or classification, and how they can be applied in a university setting.
✨Showcase Your Leadership Skills
As a potential AI Director, you'll need to demonstrate your ability to lead cross-functional teams. Prepare examples of how you've successfully managed diverse groups and driven projects from concept to production.
✨Prepare for Ethical Discussions
Given the emphasis on Responsible AI, be ready to talk about ethical considerations in AI deployment. Think about how you would address bias, privacy, and compliance in your projects, and have some real-world examples at hand.
✨Have a Vision for the Future
Articulate your vision for AI in higher education. What high-impact use cases do you see? How would you prioritise initiatives? This will show your strategic thinking and alignment with the company's goals.