Overview
The role focuses on building, structuring, and industrialising AI solutions to support the Organisation's adoption of advanced AI capabilities and contribute to the development of internal AI capabilities.
Key Responsibilities
- Design and develop a reusable framework for agentic AI solutions.
- Define and implement reference architectures, reusable components, libraries, templates, and standard patterns for AI and agent-based systems.
- Produce technical documentation, guidelines, and best practices.
- Evaluate and integrate relevant tools, frameworks, and approaches.
- Contribute to the design, development, and testing of AI applications, including prototyping, RAG pipelines, agents, and AI services.
- Integrate AI solutions with enterprise systems and data sources.
- Transform proofs of concept into production-ready tools, ensuring performance, scalability, security, compliance, maintainability, supportability, and financial sustainability.
- Contribute to deployment, packaging, and release processes.
- Support handover to operational teams, including documentation and knowledge transfer.
- Develop reusable assets and components.
- Keep abreast of advances in AI trends, technologies, methodologies, and best practices.
- Promote excellence and contribute to improving effectiveness and efficiency.
Required Experience
- Hands-on engineering mindset and attention to technical quality.
- Ability to work autonomously on complex technical topics.
- Pragmatic, solution-oriented and delivery-focused approach.
- Strong problem-solving skills and adaptability.
- Experience in international or multilateral environments is highly desirable.
- Hands-on experience with Generative AI technologies, including LLMs, embeddings, Retrieval-Augmented Generation (RAG), and prompt engineering.
- Proven experience building agent-based or multi-step AI systems.
- Practical experience with at least one major GenAI platform.
- Experience integrating AI services into existing applications and enterprise environments.
- Hands-on experience with open-weight models and open-source AI stacks.
- Experience with model deployment and optimisation techniques.
- Strong programming skills in Python, with experience building production-grade applications.
- Experience developing APIs, services, and pipelines integrating AI components.
- Familiarity with vector databases, embedding pipelines, indexing and retrieval strategies, and structured/unstructured data processing.
- Solid engineering practices (testing, version control, CI/CD, monitoring).
- Experience contributing to the setup or operation of AI platforms or shared AI services.
- Understanding of authentication, access control, API management, scalability, and usage monitoring.
- Understanding of risks related to AI systems and experience implementing technical safeguards.
- Ability to operate within enterprise security and governance constraints.
Qualifications
Advanced university degree in Artificial Intelligence, Data Science, Computer Science, Economics, Public Policy or a related field, or equivalent practical experience.
