(Senior) Digital Program Specialist

(Senior) Digital Program Specialist - AI Model Engineer The Asian Infrastructure Investment Bank (AIIB) is a multilateral development bank whose mission is financing Infrastructure for Tomorrow in Asia and beyond - infrastructure with sustainability at its core. We began operations in Beijing in 2016 and have since grown to 111 approved members worldwide. We are capitalized at USD100 billion and AAA-rated by the major international credit rating agencies. Collaborating with partners, AIIB meets clients' needs by unlocking new capital and investing in infrastructure that is green, technology-enabled, and promotes regional connectivity.
The Information Technology Department (ITD) provides technical services in the areas of digital services, IT-related procurement, cybersecurity, IT risk and resilience, data management, digital learning, and digital transformation, ensuring their overall alignment with the Bank's needs and priorities. The team oversees the development and refinement of the IT strategy as well as the effective management of technology resources and the provision of technical support across Bank operations. These efforts are critical to fostering a digital and data-driven culture within the Bank aligned with its Corporate Strategy, promoting the innovation of digital infrastructures, and ensuring the smooth operation and security of daily functions.
The (Senior) Digital Program Specialist - AI Model Engineer will be responsible for evaluating, adapting and optimizing AI models and their application within the Bank's business and knowledge context. The role combines model evaluation and applied AI engineering with semantic modelling, ontology and knowledge-grounding capabilities to improve the accuracy, relevance and reliability of enterprise AI applications and agents. The role will also contribute to model and technology selection, retrieval and agent design, prototyping, integration and solution evaluation, working with AI product managers, engineers and platform teams to deliver secure, scalable and maintainable AI capabilities. The position requires working closely with the Data Management Department, business stakeholders and domain experts.

Responsibilities:

• Evaluate and benchmark AI models and model-based solutions against the Bank's business scenarios, assessing accuracy, relevance, hallucination, safety, latency, reliability and cost.
• Develop representative evaluation datasets, test scenarios and acceptance criteria with AI product managers and business experts, including tests based on institutional data and knowledge.
• Recommend suitable models and configurations, and optimize prompts, context and model settings to improve performance for priority use cases. Assess finetuning where justified.
• Conduct controlled experiments to distinguish weaknesses in model behavior, retrieval, knowledge sources and semantic interpretation, and recommend targeted improvements.
• Monitor model and solution quality following deployment, assess the impact of model or configuration changes, and maintain evaluation methods and documented results.
• Design and implement domain ontologies, semantic models and knowledge graphs that help AI applications interpret institutional data and knowledge consistently.
• Translate business concepts, definitions and relationships agreed with data and business domain experts into technical semantic structures and mappings.
• Connect structured data, documents and metadata through consistent entities, identifiers and relationships. Implement or guide entity resolution and semantic enrichment.
• Establish validation, versioning and quality-control practices for technical semantic assets, supporting their reuse across AI use cases and bank-wide data and knowledge assets.
• Design and implement knowledge-grounding and retrieval approaches that enable AI models and agents to use authoritative institutional information and relevant business context.
• Apply retrieval-augmented generation, semantic search, knowledge graphs or GraphRAG where appropriate, evaluating their contribution to answer quality, traceability and cost.
• Integrate model, retrieval and semantic capabilities into AI applications and agent workflows through suitable APIs, tools and platform services.
• Preserve source references, access restrictions and relevant metadata, and work with data and business departments to ensure knowledge use reflects agreed definitions, ownership and information-management requirements.
• Translate prioritized business and product requirements into practical technical designs for model use, retrieval, knowledge integration and agent behavior.
• Lead prototypes and proofs of concept to validate model suitability, semantic approaches and solution feasibility, and provide hands-on engineering and integration support.
• Work with AI pro