Job Summary
Good understanding of Generative AI, large language models, Agentic AI concepts, prompt engineering, and responsible AI practices. • Experience identifying and prioritizing automation opportunities based on business value, feasibility, risk, and return on investment. • Working knowledge of automation platforms and techniques such as Power Automate, RPA, scripting, workflow orchestration, AIOps, or equivalent tools. • Awareness of enterprise AI patterns such as Retrieval-Augmented Generation (RAG), knowledge assistants, intelligent ticket classification, automated reporting, and predictive operations. • Ability to translate operational pain points into clear AI/automation use cases, requirements, success measures, controls, and implementation roadmaps. • Ability to work with engineering teams on proof-of-concept validation, data readiness, integration, security, governance, adoption, and value realization.
Key Responsibilities
Lead end-to-end delivery of infrastructure projects, upgrades, migrations, releases, and service-improvement initiatives across one or more business units. • Own project planning, scope, schedules, budgets, resources, dependencies, risks, issues, and delivery milestones. • Provide clear governance through status reporting, cost tracking, RAID management, escalation, and executive stakeholder communication. • Coordinate with business, infrastructure, application, security, operations, vendors, and enterprise partners to define requirements and implementation plans. • Ensure project artifacts, technical procedures, operational documentation, user guides, handover documents, and audit evidence are complete and compliant. • Lead production implementation planning, readiness reviews, cutover activities, outage assessments, remediation, and post-implementation reviews. • Drive service performance against agreed SLAs and KPIs, including availability, incident resolution, change success, customer satisfaction, and operational efficiency. • Identify high-value AI and automation opportunities within incident, request, change, monitoring, reporting, capacity, asset, and knowledge-management processes. • Partner with technical teams to deliver AI/automation solutions from opportunity assessment and proof of concept through deployment, adoption, benefit tracking, and optimization. • Promote Agile ways of working and continuous improvement across delivery and support teams.
Skill Requirements
18+ years of technical project management, infrastructure delivery, or service delivery management experience. • Proven experience managing complex infrastructure projects and production environments requiring high availability. • 15+ years of hands-on Agile delivery experience using tools such as Jira, Kanban, and Confluence. • Strong understanding of the software/service delivery lifecycle, IT operations, change management, incident management, problem management, and production support. • Demonstrated knowledge or experience in AI and automation initiatives, preferably within infrastructure operations or IT service management. • Strong financial and commercial management skills, including budgeting, forecasting, cost control, resource planning, and benefits tracking. • Excellent stakeholder management and communication skills, including engagement with senior leadership and customers. • Strong organizational, analytical, problem-solving, and decision-making skills, with the ability to work under pressure. • Flexibility to support critical implementations, meetings, or production events outside standard business hours when required. Preferred Certifications • PMP, PRINCE2, ITIL, Scrum Master, or equivalent project/service-management certification. • Microsoft Azure AI, Azure Fundamentals, Power Platform, automation, cloud, or AIOps-related certification. • Certification or formal learning in Generative AI, responsible AI, or AI for project/service management.
Other Requirements
Key Competencies • Delivery ownership and accountability • Customer and stakeholder management • Infrastructure project governance • Risk, compliance, and audit readiness • Financial and resource management • AI and automation opportunity discovery • Data-driven decision-making • Continuous improvement and innovation