Revenue Improvement and Spending Efficiency Program-for-Results Operation
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Published
25 May 2026
Closing date
Not listed
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Tender summary
Republic of Rwanda Ministry of Finance and Economic Planning P.O Box 158 Kigali, Rwanda ---------------------------------------------------------------------------------------------- REQUEST FOR EXPRESSIONS OF INTEREST Recruitment for Individual Consultant Services (1st Senior AI/ML Engineer) for the Integrated Financial Management Information System (IFMIS) Background The Government of Rwanda (GoR) is implementing the Revenue Improvement and Spending Efficiency (RISE) Program-for-Results (PforR) Operation with support from the World Bank. The Program aims to strengthen domestic revenue mobilization, improve efficiency in public spending, and enhance transparency and accountability in public financial management systems. The Integrated Financial Management Information System (IFMIS) supports end-to-end public financial management processes, to improve operational efficiency, data quality, risk management, and decision support, IFMIS intends to implement AI and Machine Learning (ML) solutions that enhance forecasting, anomaly detection, process optimization, and user support, while maintaining strict requirements for security, auditability, compliance, and system performance Objective The Government of Rwanda seeks to engage two (2) Senior AI/ML Engineer Individual Consultants through a single competitive selection process, from which two separate contracts will be awarded to the first and second ranked candidates. Each consultant will be engaged under an independent contract and will be individually accountable for the full scope of duties described in this Terms of Reference. The consultants will design, develop, deploy, and maintain AI/ML solutions that enhance IFMIS business processes and analytics. Their work will focus on building production-ready, explainable, secure, and maintainable AI capabilities that seamlessly integrate with the IFMIS architecture and data environment. Although each consultant will operate under a separate contract, they are expected to collaborate within the same technical ecosystem to ensure timely delivery, coherence, and continuity of AI/ML operations across the IFMIS platform. Scope of the Services Under the supervision of the Financial Systems Development Programme Manager the two senior AI/ML Engineer Individual Consultants will work closely with IFMIS business teams, data engineers, software engineers, security teams, and stakeholders in the delivery of the assignment. The two consultants will operate within the same technical environment and are expected to coordinate their efforts to maximize efficiency, ensure continuity of AI/ML operations, and meet agreed delivery timelines. The Individual Consultants will: Implement the already identified and approved AI/ML use cases for IFMIS by working with IFMIS business team and technical stakeholders to confirm objectives, clarify business rules, validate data readiness, define measurable success criteria, and translate each use case into a practical delivery plan. This includes confirming expected outputs, usage in IFMIS workflows, integration touchpoints, and operational constraints such as auditability, performance, security, and compliance. Assess and prepare the required IFMIS datasets needed to deliver the approved AI/ML use cases, including profiling data quality, defining cleaning and validation rules, and implementing feature engineering logic that is traceable and reproducible. Work closely with data engineering and database teams to ensure data pipelines support reliable training and inference, with proper documentation of data lineage, feature definitions, and consistent alignment to IFMIS master data structures. Design, develop, train, and evaluate machine learning and/or NLP models aligned to the approved IFMIS use cases, selecting appropriate approaches and baselines and improving them iteratively based on agreed metrics. Define evaluation methodologies, performance thresholds, and validation processes that fit the operational and
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