Overview
Lead the Core Systems Engineering team, setting technical direction and standards for platforms, APIs, components, and tools that underpin UNOPS digital solutions, including specialized AI products.
Key Responsibilities
- Provide technical leadership to Systems Architects and Developers, setting engineering standards and ensuring quality delivery.
- Define and enforce technical standards, coding practices, and architectural patterns.
- Make technology decisions for the CSE stack, balancing innovation with maintainability.
- Recruit, mentor, and develop team members.
- Guide the adoption of AI-assisted development tools and practices.
- Plan and manage the CSE engineering roadmap.
- Own the design and delivery of CSE's core outputs: APIs, reusable components, integration layers, AI application templates, project templates, and developer tools.
- Define cloud hosting architectures and deployment specifications.
- Design and oversee CI/CD pipelines, branching strategies, and application deployment workflows.
- Ensure CSE-built platforms and components are production-ready, well-documented, and designed for handover.
- Manage the developer experience.
- Partner with the AI Center of Excellence Lead to prioritize field requirements.
- Coordinate with the IT Infrastructure team on hosting environments and support.
- Coordinate with the Business Applications team on component and platform needs.
- Establish and maintain engineering governance, including architecture review processes and security-by-design practices.
- Report to ITG leadership on CSE delivery progress, technical risks, capacity, and resource needs.
Required Experience
- Minimum 7 years of relevant experience with a Master's degree or 9 years with a Bachelor's degree in software engineering, system architecture, or a related field.
- Minimum 3 years of technical leadership experience.
- Experience designing and delivering platforms, reusable component libraries, or Enterprise APIs.
- Experience with .NET Core, Python, Angular, or Google Cloud Platform.
- Demonstrated experience with Google Cloud Platform facets for building, testing, and deploying enterprise applications.
- Experience building or integrating AI/ML components into production systems.
- Experience establishing technical standards, architecture governance, or engineering quality frameworks.
Qualifications
- Bachelor’s degree (or equivalent) in Computer Engineering, Computer Science, Software Engineering, a related technical discipline, or equivalent or related fields.
- Master’s degree (or equivalent) in any of the above or related fields.
