Senior AI Engineer

Senior AI Engineer - Full-time - Directorate: DKI - Grade: CF5 ## Company Description The Organisation for Economic Co-operation and Development (OECD) is an international organisation comprised of 38 member countries, that works to build better policies for better lives. Our mission is to promote policies that will improve the economic and social well-being of people around the world.  Together with governments, policy makers and citizens, we work on establishing evidence-based international standards, and finding solutions to a range of social, economic and environmental challenges. From improving economic performance and creating jobs to fostering strong education and fighting international tax evasion, we provide a unique forum and knowledge hub for data and analysis, exchange of experiences, best-practice sharing, and advice on public policies and international standard-setting. ## Job Description The Executive Directorate (EXD) stewards OECD resources on behalf of the Secretary-General, with responsibility for people and wellbeing, budget management, security, infrastructure, and the OECD’s convening activities. EXD provides corporate services and strategic advice on management and corporate policies to the Secretary-General, Council and Standing Committees, and delivers compliance and risk management functions within its remit, operating in a fast-paced environment focused on management excellence. Within the Executive Directorate (EXD), the Digital, Knowledge and Information Service (DKI) is responsible for overseeing corporate IT infrastructure and operations, maintaining digital security, information management, and delivering digital and other IT-related products and services. EXD/DKI also supports the safe adoption of artificial intelligence (AI) technologies across the Organisation. Following the recommendation of the OECD AI Task Force and in line with the evolving AI-related requirements of the OECD, the Corpoate AI Office (CAIO) division has been established to support the safe adoption of AI technologies across the Organisation. CAI is tasked with overseeing the shift from initial experimentation towards the scaled adoption of AI (from Exploration to Institutionalisation) that delivers tangible value with a clear emphasis on enhancing organisational impact, improving productivity, and transforming ways of working. In this role, you will sit within the AI Lab in EXD/DKI/CAIO. The AI Lab supports the development and operationalisation of the OECD’s corporate AI framework by identifying, designing and implementing high-value AI solutions that strengthen the Organisation’s analytical and operational capabilities. You will be focused on building, structuring and industrialising AI solutions to support the Organisation’s adoption of advanced AI capabilities. You will also contribute to the development of internal AI capabilities. Please note: We wish to create a roster of candidates for potential future roles of a similar nature. Therefore, we encourage aspiring candidates to apply, even in cases where they may not meet all of the listed criteria. Main Responsibilities Design and Implement an Internal AI Engineering Framework - Design and develop a reusable engineering framework for AI and agent-based solutions across the Organisation. - Define reference architectures, reusable components, templates and standard development patterns for AI applications, workflows and integrations. - Establish practical technical guidance and engineering standards to support the consistent, secure and scalable development of AI solutions. - Evaluate relevant technologies, platforms and approaches, taking account of the rapidly evolving AI ecosystem and the OECD’s enterprise requirements. - Contribute to a coherent Organisation-wide approach to AI architecture, implementation and governance. Identify and Design High-Value AI Solutions - Work with OECD Directorates and internal teams to identify, scope and structure high-value AI use cases. - Analyse business requirements and existing workflows, identifying where AI could improve organisational impact, productivity or ways of working. - Translate business needs into clear technical requirements, solution designs and implementation approaches. - Assess potential solutions in terms of feasibility, information sensitivity, security, cost, scalability, interoperability and operational sustainability. - Prioritise practical and reusable approaches that can deliver value across multiple business areas where appropriate. Build and Implement AI Solutions - Lead and contribute directly to the design, development, testing and evaluation of AI applications, services and workflows. - Develop prototypes and proofs of concept, using rapid iteration to validate technical assumptions and user requirements. - Implement AI capabilities such as retrieval-augmented generation, agent-based workflows, data pipelines and integrations with enterprise systems. - Select appropriate commercial, open-source or open-weight technologies according to the needs and constraints of each use case. - Work iteratively with business users and technical colleagues to ensure that solutions are effective, usable, robust and aligned with organisational requirements. Industrialise and Operationalise AI Use Cases - Transform proofs of concept (PoCs) and experimental solutions into production-ready tools. - Ensure solutions meet enterprise requirements in terms of: - Performance and scalability - Security and compliance - Maintainability and supportability - Financial sustainability - Contribute to deployment, packaging and release processes. - Support handover to operational teams, including documentation, knowledge transfer and alignment with support models. - Develop reusable assets and components to accelerate future implementations. Collaboration & Continuous Improvement - Provide technical advice to business and technology teams on the appropriate use of AI tools, models and engineering approaches. - Communicate technical concepts, architectural choices, implementation considerations and responsible AI practices clearly to both specialist and non-specialist stakeholders. - Keep abreast of advances on emerging industry trends, related technologies, methodologies and best practices. - Promote excellence and contribute to improving effectiveness, efficiency and division-level excellence. - Contribute to other related tasks within EXD/DKI, as required. ## Qualifications Ideal Candidate Profile Academic Background - Advanced university degree in Artificial Intelligence, Data Science, Computer Science, Economics or a related field, or equivalent practical experience. Professional Background - Significant experience designing, developing and implementing AI-enabled applications, services or enterprise solutions. - Demonstrated ability to take AI solutions from initial concept and prototyping through to operational deployment. - Ability to work autonomously on complex technical topics. - Pragmatic, solution-oriented and delivery-focused approach. - Strong problem-solving skills and adaptability in a fast-evolving field. - Experience in international or multilateral environments is highly desirable. Tools The successful candidate would demonstrate/possess several of the following: - Major generative AI platforms and model ecosystems. - Retrieval-augmented generation, agent-based systems and AI workflow orchestration. Demonstrated MCP (Model Context Protocol) experience and skills. - Python-based application, API and data-pipeline development. - Enterprise integration and the processing of structured and unstructured data. - Cloud-based, on-premises or hybrid AI deployment environments. - Software engineering practices including testing, version control, deployment, monitoring and lifecycle management. - Security controls, access management and responsible AI safeguards. Languages - Fluency in one of the two OECD official la