Jobs in Uganda: Find Your Next Career Opportunity
Access the most comprehensive list of job vacancies in Uganda. From NGO positions in Kampala to government roles and private sector opportunities across the country.
Results for "Mbarara"
2 jobs foundInformation and Technology OfficerHOT NOW
Job at Mbarara District Service Commission. Source: Great Uganda Jobs
Machine Learning EngineerHOT NOW
s & requirements Job Title: Machine Learning Engineer Department/Group: VENNReporting To: Senior Data Scientist Years of Experience: 3+ yearsLocation: Mbarara Travel Required: Up to 30%About Raising The VillageAt Raising The Village (RTV), we are dedicated to eradicating ultra-poverty in Sub-SaharanAfrica. As a dynamic, rapidly growing international development organization, we've assembleda team of over 250 passionate individuals in Uganda, alongside an additional 17 professionalsin North America and 15 in Rwanda. Together, we are committed to elevating communities outof ultra-poverty by implementing innovative solutions and leveraging advanced data analytics todrive impact.To date, our holistic approach has positively impacted over 1 million lives since2012, and we're poised to achieve even greater milestones, aiming to assist 1 million individualsannually by 2027. Our growth and success are fueled by the invaluable support of globalpartners who share our vision of sustainable change. Learn more about our impactful programsat www.raisingthevillage.orgThe VENN department is the data and technology backbone of our organization, connectingadvanced analytics, and custom software tools with field implementation to ensuredata-informed decision-making at every level.Job DescriptionThe Machine Learning Engineer is responsible for building, deploying, and continuouslyimproving RTV's production LLM applications, which are currently live across multiple platformsand actively used by field teams and program staff across Uganda, Rwanda, and theDemocratic Republic of Congo. The role sits within the Predictive Analytics / VENN departmentand focuses on advancing agentic LLM architectures, RAG systems, and evaluationinfrastructure as RTV scales its AI capabilities to new countries and deepens integration withmobile field tools and the data warehouse. A core area of responsibility is the SBCC (Social andBehavior Change Communication) system, which generates personalized, practice-specificbehavior change messaging for field officers across agriculture, health, livestock, andcommunity domains, and is currently being integrated into RTV's mobile check-in application.The engineer will work closely with the Data Engineer, Data Scientists, the SoftwareEngineering team, and field program teams to deliver reliable, context-aware LLM applicationsthat integrate with RTV's data warehouse, mobile implementation apps, and the broaderWorkMate AI ecosystem. This role also contributes to RTV's strategic partnership with TheAgency Fund (TAF) AI Accelerator, supporting shared technical challenges in knowledge basearchitecture, multi-country scaling, and LLM evaluation governance.Key Responsibilities Design and implement agentic LLM architectures including multi-step reasoningpipelines, tool use, memory management, and autonomous workflow orchestration usingLangChain and related frameworks, applied across both conversational and generativeAI use cases. Build, maintain, and optimize Retrieval-Augmented Generation (RAG) pipelines forcontext-grounded LLM responses, including embedding strategy design, chunkingapproaches, and retrieval optimization tailored to diverse content types such as programdocumentation, household data, and behavioral practice guidelines. Manage and evolve RTV's vector database infrastructure (Chroma or Qdrant) includingindex management, namespace organization, and multi-domain retrieval tuning tosupport distinct organizational use cases. Design, build, and maintain end-to-end ML pipelines covering data ingestion, featureengineering, model training, evaluation, and deployment, ensuring reproducibility andversion control across all pipeline stages. Apply knowledge of core ML algorithms — including supervised learning, classification,regression, clustering, and neural network architectures — to select appropriatemodeling approaches for diverse problem types across RTV's AI workstreams. Develop and manage the full LLM application lifecycle — from prompt engineering andchain construction through deployment, versioning, and production monitoring — usingLangChain and LangSmith as the primary development and observability stack. Design and implement LLM evaluation frameworks using LLM-as-a-judge approaches,automated metrics, and human evaluation protocols to assess response quality, factualgrounding, cultural appropriateness, and content safety across generative outputs. Instrument production LLM applications with LangSmith tracing, logging, and feedbackcollection pipelines to enable continuous performance monitoring, failure analysis, anditerative improvement cycles. Build and deploy RESTful API endpoints for LLM-powered services, ensuring stableintegration with WorkMate and the RTV mobile implementation app used by field officersduring household visits. Develop and maintain personalized content generation pipelines that leveragehousehold segmentation, behavioral data, and program-specific context from the datawarehouse to produce targeted, practice-specific outputs at scale. Implement offline and low-connectivity strategies including message caching andfallback mechanisms to ensure AI-powered tools remain accessible to field officers inremote locations. Collaborate with the Applied Learning team to incorporate validated program content intoknowledge bases and generation templates, ensuring evidence-based alignment andcontent quality across all LLM outputs. Write clear technical documentation for agent architectures, RAG pipeline designs,evaluation frameworks, and API specifications to support team collaboration andorganizational knowledge continuity.Technical Requirements Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science,Statistics (Computing Major)or a related quantitative field. 3+ years of hands-on experience building and deploying production LLM applications,with a demonstrable portfolio. Proficiency in:○ LangChain for agentic pipeline construction, tool use, memory integration, andRAG implementation.○ LangSmith for LLM application tracing, evaluation, dataset management, andproduction monitoring.○ Vector databases (Chroma and/or Qdrant) including embedding management,indexing, and retrieval optimization.○ Agentic design patterns including ReAct, plan-and-execute, multi-agentorchestration, and tool-augmented reasoning.○ LLM evaluation methodologies including LLM-as-a-judge frameworks,reference-based and reference-free metrics, and human-in-the-loop evaluationworkflows.○ Python for LLM application development, API construction (FastAPI orequivalent), and pipeline automation.○ OpenAI API and prompt engineering best practices including few-shot prompting,structured output generation, and system prompt design.○ Cloud deployment on AWS, including containerized application hosting,environment management, and API infrastructure. Experience integrating LLM applications with structured data sources (SQL databases,data warehouses) for analytics-augmented generative AI capabilities. Solid understanding of core ML algorithms including supervised and unsupervisedlearning, classification, regression, ensemble methods, and neural networkarchitectures, with the ability to select and apply appropriate approaches for variedproblem types. Hands-on experience building and managing ML pipelines including data preprocessing,feature engineering, model training, evaluation, experiment tracking (Weights & Biasesor equivalent), and production deployment using CI/CD practices. Familiarity with mobile application integration and offline-first design patterns forlow-connectivity deployment environments is an asset.Personal Attributes Genuine commitment to using AI for social impact and poverty alleviation in last-milecommunities. Strong engineering discipline with attention to reliability, safety, and cultural sensitivity inAI-generated content. Ability to translate complex LLM system outputs into accessible insights for non-technicalfield staff and program managers. Collaborative and communicative team player who can work across analytics, softwaredevelopment, and field program teams. High degree of ownership, intellectual curiosity, and drive to stay current with thefast-moving LLM engineering landscape.Raising The Village is committed to Equity and Inclusion in the workplace and is proud to be anequal opportunity employer.< Log In and Apply
Career Resources 🚀
📄 CV Writing
Tailor your CV for every single job application. Quantification is key—instead of just listing duties, highlight your achievements with numbers (e.g., "Increased sales by 20%").
🤝 Interview Prep
Research the company's mission and values before the interview. Practice your "elevator pitch" and be ready to explain how your skills solve their specific problems.
🌐 Networking
Leverage professional associations and alumni networks in Uganda. Many jobs are filled through referrals before they even reach public job boards.