(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 product managers, engineers and platform teams to select appropriate technologies and delivery pathways, reusing existing capabilities where suitable.
• Guide testing and production transition, including technical documentation, monitoring and support arrangements, in alignment with architecture, cybersecurity and responsible AI requirements.
• Develop reusable evaluation datasets, model scorecards, semantic assets, mappings and implementation patterns to support consistent delivery and reduce duplicated effort.
• Work with data and business departments, which retain responsibility for business definitions, data ownership and governance, while implementing AI and semantic capabilities within ITD’s remit.
• Provide technical input to vendor assessments, procurement, solution reviews and AI platform improvements, communicating findings and trade-offs clearly.
• Monitor developments in AI models, evaluation, semantic technologies and knowledge engineering, and translate relevant advances into practical recommendations and internal knowledge sharing.

Requirements:

• 8-12 years of relevant experience in AI or machine learning engineering, knowledge engineering, semantic technologies, software engineering or related technical roles.
• Bachelor's degree in computer science, engineering, information systems, data science or a related field. An advanced degree is an advantage.
• Strong understanding of AI models, particularly large language models, including model selection, configuration, evaluation, limitations and performance optimization.
• Practical expertise in semantic modelling, ontology design or knowledge graph engineering, and their application to enterprise data, institutional knowledge and AI solutions.
• Strong understanding of knowledge grounding and retrieval, including retrieval-augmented generation, semantic search and graph-based approaches, with the ability to assess when each is appropriate.
• Hands-on capability in programming and applied AI engineering, including model APIs, evaluation pipelines, data mapping, retrieval and integration with enterprise platforms.
• Understanding of AI agent workflows, cloud-based AI services and enterprise security, with the ability to assess technical options against quality, cost, scalability and maintainability.
• Demonstrated experience evaluating, adapting or implementing AI models and applications for practical business use cases.
• Demonstrated hands-on experience designing or implementing ontologies, semantic models or knowledge graphs, including mapping business concepts to data or information sources.
• Experience working with business domain experts and technical teams to prototype, test and integrate AI or knowledge-based capabilities into operational solutions.
• Experience in financial institutions, multilateral development banks or other complex institutional environments is highly desirable.
• Strong architecture thinking, systems analysis, problem-solving and technical judgement, combined with a pragmatic and delivery-oriented approach.
• Ability to work across business, product, data, platform, architecture, security and engineering disciplines and influence without direct authority.
• Ability to explain technical concepts, assumptions, trade-offs and recommendations clearly to technical and non-technical audiences.
• Strong collaboration, facilitation and stakeholder-management skills in multidisciplinary and matrixed environments.
• Relevant certifications in cloud, AI, solution design, architecture, data or semantic technologies are an advantage.
• Commitment to continuous learning in enterprise AI, semantic technologies and emerging digital capabilities.
• Excellent written and verbal communication skills in English.

AIIB is committed to diversity, transparency, and inclusion. We believe our strength comes from having a team with the right diverse skills, experiences, and abilities selected through a merit-based competitive process. We actively encourage applications from people from both within and outside AIIB members, regardless of nationality, religion, gender, race, disability, or sexual orientation.

Join in AIIB’s mission to promote sustainable infrastructure investments and to improve social and economic outcomes in Asia and beyond.

Previous experience and qualifications will determine the grade and job title at which successful applicants will enter AIIB.