AI-Native Delivery Manager

Job Description

JRC Software is looking for an AI-Native Delivery Manager to oversee delivery across multiple software projects and client engagements, ensuring predictable outcomes, strong governance and efficient resource utilisation. This role will drive both delivery excellence and the transformation of JRC's delivery model through AI. You will be accountable for identifying, implementing and scaling AI-enabled workflows that deliver measurable improvements in productivity, quality, delivery speed and cost efficiency.

Delivery Management

• Oversee multiple software projects and client engagements, ensuring delivery within agreed scope, timelines, budgets and quality standards.
• Coordinate Project Managers, technical leads and cross-functional teams to maintain delivery momentum and accountability.
• Monitor project progress, milestones, dependencies, risks and issues, implementing corrective actions when required.
• Manage client expectations, escalations and delivery-related concerns.

Governance and Performance Management

• Establish and maintain delivery governance frameworks, reporting mechanisms and performance monitoring processes.
• Oversee resource allocation, capacity planning, workload balancing and utilisation across projects.
• Track planned versus actual effort, budgets, delivery performance and scope changes.
• Prepare delivery dashboards and reports highlighting progress, risks, variances and corrective actions for senior management.
• Establish clear delivery standards, accountability mechanisms and continuous improvement practices.

AI-Native Execution and Adoption

• Own the adoption and implementation of AI-enabled delivery practices across JRC's software development lifecycle and delivery operations.
• Identify high-impact opportunities to apply AI tools, agents and automation across requirements analysis, development, testing, documentation, project management and reporting.
• Work with technical and functional leads to implement, validate and scale AI-enabled workflows across projects.
• Establish reusable AI workflows, automation patterns, guidelines and best practices that teams can consistently apply.
• Define baseline metrics and track improvements in delivery lead time, manual effort, productivity, defect rates, rework and project costs.
• Evaluate AI initiatives based on measurable business value, adoption, reliability and cost-effectiveness, discontinuing or refining approaches that do not deliver expected results.
• Ensure appropriate human oversight, quality validation and compliance with client confidentiality, security and data protection requirements.
• Drive team adoption through practical guidance, knowledge sharing and continuous improvement.

Requirements

• Bachelor's degree in IT, Computer Science, Software Engineering or a related field, or equivalent practical experience.
• 5+ years of experience in software delivery, project management or technology operations.
• Proven experience managing multiple software projects, cross-functional teams and client engagements.
• Strong understanding of SDLC, Agile methodologies, delivery governance, risk management and change control.
• Experience in resource planning, capacity management, effort tracking, budgeting and delivery reporting.
• Practical experience implementing AI-assisted workflows or automation, with the ability to demonstrate measurable improvements in efficiency, quality or delivery outcomes.
• Ability to translate AI opportunities into practical solutions, drive team adoption and establish repeatable processes.
• Strong leadership, stakeholder management, analytical and problem-solving skills.
• Familiarity with Jira, Azure DevOps, GitHub or similar delivery and engineering platforms.
• Experience working with international clients and distributed teams is an advantage.

Success Measures

• Delivery performance: Projects meet agreed timelines, scope, budget and quality expectations.
• AI-driven productivity: Demonstrable improvements against established baselines in delivery time, manual effort, rework or cost.
• Adoption and scalability: AI-enabled workflows are actively used by teams and reused across multiple projects where appropriate.
• Governance and visibility: Accurate reporting, proactive risk management and clear accountability across the delivery portfolio.
• Quality and client outcomes: Maintained or improved software quality, client satisfaction and delivery reliability.