AI Director

Global University Systems; Manchester; On-site; GBP 80,000 - 100,000; Full time

Job summary

A leading educational institution in Manchester is seeking a Director of AI to lead transformative AI initiatives across multiple higher education institutions. The candidate will own the AI roadmap end-to-end, develop and manage cross-functional teams, and ensure governance and compliance. This role demands 6+ years in IT delivery with extensive experience in AI/ML initiatives, alongside a Bachelor's or Master's in a relevant field. Join us to shape the future of AI in higher education.

Benefits

Competitive salary

Health benefits

Professional development opportunities

Qualifications

• 6+ years in IT delivery, product, or programme leadership roles, including at least 3 years in AI/ML initiatives.
• Proven track record running Agile PoCs and MVPs within complex organisations.
• Hands-on experience with cloud platforms (e.g., Azure, AWS, GCP) and MLOps tools.

Responsibilities

• Own the AI roadmap and manage AI initiatives portfolio.
• Run PoCs and MVPs using Agile methods.
• Implement AI governance frameworks and ensure compliance.

Skills

Product and outcome mindset

Delivery excellence

Technical fluency

Responsible AI and data literacy

Change leadership

Education

Bachelor's or Master’s degree in Data Science, Computer Science, Business Analytics, or a related discipline

Tools

Azure ML

Databricks

Kubernetes

Docker

Job description

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.

In this role you will own 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, optimisation support, and conflict detection.
• Teaching & Learning: content summarisation, 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).

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 optimise 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 programme 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 organisations.
• 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.