AI Director in London

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.

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.