AI Programme Manager, Machine Learning Delivery Lead, Intelligent Automation Lead

Identify high-value use cases, assemble cross-functional squads, oversee model development, manage data governance, and report performance metrics to senior leadership. Uses cloud machine-learning platforms, experimentation trackers, and ethics checklists.

Retail personalisation teams | Financial fraud detection units | Manufacturing predictive maintenance divisions | Health diagnostics platforms | Public sector innovation hubs | Consultancy AI practices

Practitioners often come from data engineering or software backgrounds, upskill in machine-learning concepts, then manage pilot projects. Successful pilots and an understanding of regulatory landscapes enable progression to strategic oversight roles spanning multiple departments.

AI adoption is accelerating, yet responsible implementation skills are scarce. Experienced leaders can move into director of data science, chief data officer, or advisory roles shaping national guidelines on trustworthy AI.

Use-case discovery and feasibility scoring frameworks | Data pipeline design for model training | Model evaluation and drift monitoring dashboards | Responsible AI and bias mitigation techniques | Scalable inferencing architecture on cloud services | Budgeting for compute and licensing costs | Experiment lifecycle management tools

Ethical judgment balancing innovation and risk | Storytelling to obtain executive sponsorship | Cross-disciplinary collaboration with legal and compliance | Negotiation with vendors and research partners | Strategic roadmap planning over multi-year horizons | Change management driving adoption