Details
Mission and objectives
UNDP works in 170 countries and territories to eradicate poverty while protecting the planet. We help countries develop strong policies, skills, partnerships and institutions so they can sustain their progress
The primary and overarching objective of United Nations Development Programme in Kenya is the eradication of poverty in the context of sustainable development, including the pursuit of the Sustainable Development Goals, and promotion of United Nations fundamental principles. A core dimension to the work of UNDP in Kenya is on Democratic Governance given the national focus on governance reforms. UNDP supports the country’s efforts towards achieving the Vision 2030 Political Pillar, which envisions a democratic system that is issue-based, people-centered, results oriented and accountable to the public. This Political Pillar gears to transform the country’s political governance across five strategic areas; The Rule of Law, Electoral and Political Processes, Democracy and Public Service Delivery, Transparency and Accountability, Security Peace Building and Conflict Management. These strategic areas are anchored in the Constitution, promulgated in August 2010 marking a major milestone in the democratic journey of Kenya and set a new threshold in terms of people-centred development.
The primary and overarching objective of United Nations Development Programme in Kenya is the eradication of poverty in the context of sustainable development, including the pursuit of the Sustainable Development Goals, and promotion of United Nations fundamental principles. A core dimension to the work of UNDP in Kenya is on Democratic Governance given the national focus on governance reforms. UNDP supports the country’s efforts towards achieving the Vision 2030 Political Pillar, which envisions a democratic system that is issue-based, people-centered, results oriented and accountable to the public. This Political Pillar gears to transform the country’s political governance across five strategic areas; The Rule of Law, Electoral and Political Processes, Democracy and Public Service Delivery, Transparency and Accountability, Security Peace Building and Conflict Management. These strategic areas are anchored in the Constitution, promulgated in August 2010 marking a major milestone in the democratic journey of Kenya and set a new threshold in terms of people-centred development.
Context
In Kenya, UNDP is developing an integrated Artificial Intelligence Programme in partnership with the Ministry of Information, Communications and the Digital Economy (MICDE) and the Technopolis Development Authority (TDA). The programme connects national compute infrastructure, public-sector capability and use cases, responsible AI and data governance, and the innovation ecosystem, creating a practical pathway through which development, institutional and enterprise challenges can be identified, assessed and translated into appropriate AI-enabled solutions that can be responsibly developed, adopted and scaled.
A key component is the operationalisation of national AI compute infrastructure hosted at the Technopolis Development Authority, together with the governance, access and allocation mechanisms, technical capabilities and sustainability arrangements required for its effective use. The infrastructure supports priority use cases emerging from Government, startups and the innovation ecosystem, universities and research institutions, and UNDP Kenya programmes. National open call and a two-stage business and technical assessment, a cohort of priority use cases has been selected for supported solution development.
To operationalise this model, UNDP Kenya is mobilising complementary specialist expertise across the AI solution lifecycle. The technical team comprises a Senior Machine Learning Engineer / AI Specialist responsible for data, models and model evaluation; a Senior AI Solution Architect & MLOps Lead responsible for architecture, engineering, integration and deployment; junior AI developers; and a Responsible AI & Assurance Specialist.
The team works within the overall programme architecture under the guidance of the Artificial Intelligence Programme Manager, who retains overall portfolio leadership, prioritisation and accountability.
This UN Volunteer assignment contributes to the implementation phase of the Artificial Intelligence Programme by leading the architecture, engineering, integration and deployment of the priority AI-enabled solutions supported through the programme, and by providing day-to-day technical leadership of the AI solution development team.
A key component is the operationalisation of national AI compute infrastructure hosted at the Technopolis Development Authority, together with the governance, access and allocation mechanisms, technical capabilities and sustainability arrangements required for its effective use. The infrastructure supports priority use cases emerging from Government, startups and the innovation ecosystem, universities and research institutions, and UNDP Kenya programmes. National open call and a two-stage business and technical assessment, a cohort of priority use cases has been selected for supported solution development.
To operationalise this model, UNDP Kenya is mobilising complementary specialist expertise across the AI solution lifecycle. The technical team comprises a Senior Machine Learning Engineer / AI Specialist responsible for data, models and model evaluation; a Senior AI Solution Architect & MLOps Lead responsible for architecture, engineering, integration and deployment; junior AI developers; and a Responsible AI & Assurance Specialist.
The team works within the overall programme architecture under the guidance of the Artificial Intelligence Programme Manager, who retains overall portfolio leadership, prioritisation and accountability.
This UN Volunteer assignment contributes to the implementation phase of the Artificial Intelligence Programme by leading the architecture, engineering, integration and deployment of the priority AI-enabled solutions supported through the programme, and by providing day-to-day technical leadership of the AI solution development team.
Task description
Under the direct supervision of the UNDP Artificial Intelligence Programme Manager, and acting as technical team lead for the AI solution development team, the UN Volunteer will undertake the following tasks:
•Establish technical architecture and engineering standards for the selected AI solutions.
•Review the pre-engineering dossiers for programme-selected use cases, validate system-level assumptions and technical feasibility, close priority design gaps, and convert each approved case into a detailed solution blueprint and sequenced engineering roadmap.
•Translate each use case into an end-to-end technical architecture, defining how data sources, storage, models, applications, interfaces, infrastructure and users connect, and documenting the design decisions and trade-offs behind each choice.
•Define practical engineering standards for repositories, version control, branching and code review, data and model interfaces, reproducibility, experiment and artefact tracking, documentation, containerisation, deployment packaging, observability, and secure handling of credentials and configuration.
•Establish a structured development approach through which programme-mobilised AI developers and other technical contributors can support solution development, including technical work packages, contribution protocols, coding standards, review requirements, integration procedures and quality gates.
•Design for the provisioned on-premise NVIDIA GPU-based compute environment and associated Linux and virtualised infrastructure, while preserving portability to approved cloud or alternative compute where appropriate.
•Embed security-by-design at the engineering layer, including access control, secrets management, data protection in transit and at rest, and logging and audit considerations, in line with UNDP and Government requirements and the national Data Protection Act.
Lead data-pipeline, application and integration engineering.
•Design and implement, and technically lead junior developers and other programme-mobilised contributors in implementing, data ingestion and preparation pipelines, storage layers, APIs, backend services and application integration components required for the five (5) selected solutions.
•Integrate the models, inference code and interface specifications provided by the Senior Machine Learning Engineer / AI Specialist into coherent, testable end-to-end solutions, resolving interface, dependency and performance issues between the modelling and application layers.
•Design and implement model-serving and inference pipelines, including batching, caching, queueing, timeout and fallback behaviour appropriate to each use case.
•Translate solution blueprints into prioritised development backlogs and discrete work packages, allocate or guide development tasks, resolve technical dependencies, and ensure that individual contributions integrate into coherent end-to-end solutions.
•Establish onboarding and contribution mechanisms for participating developers, including repository access, development environments, technical briefs, interface specifications, testing requirements and documentation expectations.
•Review, test and integrate code and other technical outputs produced by contributing developers, ensuring compliance with agreed architecture, coding, security, documentation, licensing and performance standards before incorporation into programme-supported solutions.
•Design and implement integration with existing Government, startup or partner systems where required, including interface specifications, authentication, data exchange formats and error handling.
Operate compute, MLOps and deployment for the selected solutions.
•Use reproducible environments and containerised workloads, and establish clear dependency management, configuration, model and artefact versioning and test procedures so solutions can be rerun and maintained by the internal technical team.
•Establish CI/CD and MLOps practice for the selected solutions, including automated testing, build and deployment pipelines, artefact registries and promotion paths between development, test and production-like environments.
•Configure and operate the GPU and compute environment for development, training and inference workloads, including scheduling, resource allocation, environment isolation and utilisation tracking, in coordination with the technical counterparts responsible for the compute platform.
•Optimise deployment-time inference for available GPU and compute resources through appropriate batching, memory management, serving configuration and model packaging, documenting the performance and cost trade-offs of material technical choices.
•Implement monitoring and observability for deployed solutions, including system health, latency, throughput, resource consumption, error rates and the model-drift and degradation indicators defined by the Senior Machine Learning Engineer / AI Specialist.
•Advise on cloud versus sovereign and on-premise compute placement for each solution, including cost, data-residency, sustainability and operational implications.
Validate system performance and move solutions toward deployment.
•Develop and apply system-level evaluation and acceptance protocols for each selected solution, including latency, throughput, resource-use, reliability, failure-recovery and user or operational acceptance measures defined with use-case owners.
•Conduct architecture, sprint, integration, code-quality and deployment-readiness reviews, documenting issues, remediation actions and decisions in a traceable technical record.
•Maintain technical oversight across parallel development contributions, identifying dependencies, duplication, integration risks and quality issues early and directing corrective action.
•Work with the Responsible AI Specialist to implement required assurance controls and evidence within the engineering lifecycle, including data and model documentation hooks, testing, logging, monitoring and human-oversight requirements.
•Prepare five (5) selected solutions to an inference and deployment-ready standard and support controlled pilot or demonstration where required dependencies, user access and approvals are available.
•Lead the technical team, build internal capability and complete handover.
•Coordinate the junior AI developers and other programme-mobilised technical contributors on a day-to-day basis, including task allocation, sequencing, unblocking, review cadence and quality assurance of their outputs.
•Mentor technical teams and other participating contributors through paired design, code review, debugging, technical clinics and implementation support, with explicit transfer of architecture and troubleshooting knowledge.
•Foster reusable engineering practices across the programme's developer community, including common components, templates, development patterns and lessons that can accelerate subsequent AI solution development.
•Produce solution-specific technical documentation, runbooks, deployment instructions, rollback and recovery considerations, and known-limitations records for handover to the internal team.
•Prepare a consolidated technical performance and resource-use report covering the supported solution cohort, including compute consumption, bottlenecks, optimisation opportunities and reusable engineering lessons.
•Provide a final handover of source code, repositories, infrastructure and deployment configuration, documentation and reusable engineering playbooks in formats approved by UNDP, subject to applicable data, intellectual property and licensing restrictions.
Furthermore, UN Volunteers are required to:
•Strengthen their knowledge and understanding of the concept of volunteerism by reading relevant UNV and external publications and take active part in UNV activities (for instance in events that mark International Volunteer Day);
•Be acquainted with and build on traditional and/or local forms of volunteerism in the host country;
•Reflect on the type and quality of voluntary action that they are undertaking, including participation in ongoing reflection activities.
Results/Expected Outputs
•Solution architecture, engineering standards and blueprints — Validated technical scope, system-level assumptions and dependency register for the selected use cases; a detailed end-to-end solution blueprint and engineering roadmap for each programme-selected use case, including interfaces, data and model flow, compute approach, security considerations and acceptance measures; reusable development, architecture, integration, documentation and deployment standards; defined development work packages, contribution protocols, integration requirements and quality gates; and a compute and infrastructure design agreed with the compute platform counterparts.
•Establish technical architecture and engineering standards for the selected AI solutions.
•Review the pre-engineering dossiers for programme-selected use cases, validate system-level assumptions and technical feasibility, close priority design gaps, and convert each approved case into a detailed solution blueprint and sequenced engineering roadmap.
•Translate each use case into an end-to-end technical architecture, defining how data sources, storage, models, applications, interfaces, infrastructure and users connect, and documenting the design decisions and trade-offs behind each choice.
•Define practical engineering standards for repositories, version control, branching and code review, data and model interfaces, reproducibility, experiment and artefact tracking, documentation, containerisation, deployment packaging, observability, and secure handling of credentials and configuration.
•Establish a structured development approach through which programme-mobilised AI developers and other technical contributors can support solution development, including technical work packages, contribution protocols, coding standards, review requirements, integration procedures and quality gates.
•Design for the provisioned on-premise NVIDIA GPU-based compute environment and associated Linux and virtualised infrastructure, while preserving portability to approved cloud or alternative compute where appropriate.
•Embed security-by-design at the engineering layer, including access control, secrets management, data protection in transit and at rest, and logging and audit considerations, in line with UNDP and Government requirements and the national Data Protection Act.
Lead data-pipeline, application and integration engineering.
•Design and implement, and technically lead junior developers and other programme-mobilised contributors in implementing, data ingestion and preparation pipelines, storage layers, APIs, backend services and application integration components required for the five (5) selected solutions.
•Integrate the models, inference code and interface specifications provided by the Senior Machine Learning Engineer / AI Specialist into coherent, testable end-to-end solutions, resolving interface, dependency and performance issues between the modelling and application layers.
•Design and implement model-serving and inference pipelines, including batching, caching, queueing, timeout and fallback behaviour appropriate to each use case.
•Translate solution blueprints into prioritised development backlogs and discrete work packages, allocate or guide development tasks, resolve technical dependencies, and ensure that individual contributions integrate into coherent end-to-end solutions.
•Establish onboarding and contribution mechanisms for participating developers, including repository access, development environments, technical briefs, interface specifications, testing requirements and documentation expectations.
•Review, test and integrate code and other technical outputs produced by contributing developers, ensuring compliance with agreed architecture, coding, security, documentation, licensing and performance standards before incorporation into programme-supported solutions.
•Design and implement integration with existing Government, startup or partner systems where required, including interface specifications, authentication, data exchange formats and error handling.
Operate compute, MLOps and deployment for the selected solutions.
•Use reproducible environments and containerised workloads, and establish clear dependency management, configuration, model and artefact versioning and test procedures so solutions can be rerun and maintained by the internal technical team.
•Establish CI/CD and MLOps practice for the selected solutions, including automated testing, build and deployment pipelines, artefact registries and promotion paths between development, test and production-like environments.
•Configure and operate the GPU and compute environment for development, training and inference workloads, including scheduling, resource allocation, environment isolation and utilisation tracking, in coordination with the technical counterparts responsible for the compute platform.
•Optimise deployment-time inference for available GPU and compute resources through appropriate batching, memory management, serving configuration and model packaging, documenting the performance and cost trade-offs of material technical choices.
•Implement monitoring and observability for deployed solutions, including system health, latency, throughput, resource consumption, error rates and the model-drift and degradation indicators defined by the Senior Machine Learning Engineer / AI Specialist.
•Advise on cloud versus sovereign and on-premise compute placement for each solution, including cost, data-residency, sustainability and operational implications.
Validate system performance and move solutions toward deployment.
•Develop and apply system-level evaluation and acceptance protocols for each selected solution, including latency, throughput, resource-use, reliability, failure-recovery and user or operational acceptance measures defined with use-case owners.
•Conduct architecture, sprint, integration, code-quality and deployment-readiness reviews, documenting issues, remediation actions and decisions in a traceable technical record.
•Maintain technical oversight across parallel development contributions, identifying dependencies, duplication, integration risks and quality issues early and directing corrective action.
•Work with the Responsible AI Specialist to implement required assurance controls and evidence within the engineering lifecycle, including data and model documentation hooks, testing, logging, monitoring and human-oversight requirements.
•Prepare five (5) selected solutions to an inference and deployment-ready standard and support controlled pilot or demonstration where required dependencies, user access and approvals are available.
•Lead the technical team, build internal capability and complete handover.
•Coordinate the junior AI developers and other programme-mobilised technical contributors on a day-to-day basis, including task allocation, sequencing, unblocking, review cadence and quality assurance of their outputs.
•Mentor technical teams and other participating contributors through paired design, code review, debugging, technical clinics and implementation support, with explicit transfer of architecture and troubleshooting knowledge.
•Foster reusable engineering practices across the programme's developer community, including common components, templates, development patterns and lessons that can accelerate subsequent AI solution development.
•Produce solution-specific technical documentation, runbooks, deployment instructions, rollback and recovery considerations, and known-limitations records for handover to the internal team.
•Prepare a consolidated technical performance and resource-use report covering the supported solution cohort, including compute consumption, bottlenecks, optimisation opportunities and reusable engineering lessons.
•Provide a final handover of source code, repositories, infrastructure and deployment configuration, documentation and reusable engineering playbooks in formats approved by UNDP, subject to applicable data, intellectual property and licensing restrictions.
Furthermore, UN Volunteers are required to:
•Strengthen their knowledge and understanding of the concept of volunteerism by reading relevant UNV and external publications and take active part in UNV activities (for instance in events that mark International Volunteer Day);
•Be acquainted with and build on traditional and/or local forms of volunteerism in the host country;
•Reflect on the type and quality of voluntary action that they are undertaking, including participation in ongoing reflection activities.
Results/Expected Outputs
•Solution architecture, engineering standards and blueprints — Validated technical scope, system-level assumptions and dependency register for the selected use cases; a detailed end-to-end solution blueprint and engineering roadmap for each programme-selected use case, including interfaces, data and model flow, compute approach, security considerations and acceptance measures; reusable development, architecture, integration, documentation and deployment standards; defined development work packages, contribution protocols, integration requirements and quality gates; and a compute and infrastructure design agreed with the compute platform counterparts.
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