Machine Learning Engineer Job
Work Hours: Full-time, 08 hours per day
Salary:
Attractive
Job Deadline: 23 March 2026
Number of Jobs: 01
Hiring Entity: RAISING THE VILLAGE
 |
| RAISING THE VILLAGE |
Location: In Uganda
Job Details:
Job Title: Machine Learning Engineer Department/Group: VENN
Reporting To: Senior Data Scientist Years of Experience: 3+ years
Location: Mbarara Travel Required: Up to 30%
About Raising The Village At Raising The Village (RTV), we are dedicated to eradicating ultra-poverty in Sub-Saharan
Africa. As a dynamic, rapidly growing international development organization, we’ve assembled
a team of over 250 passionate individuals in Uganda, alongside an additional 17 professionals
in North America and 15 in Rwanda. Together, we are committed to elevating communities out
of ultra-poverty by implementing innovative solutions and leveraging advanced data analytics to
drive impact.To date, our holistic approach has positively impacted over 1 million lives since
2012, and we’re poised to achieve even greater milestones, aiming to assist 1 million individuals
annually by 2027. Our growth and success are fueled by the invaluable support of global
partners who share our vision of sustainable change. Learn more about our impactful programs
at www.raisingthevillage.org
The VENN department is the data and technology backbone of our organization, connecting
advanced analytics, and custom software tools with field implementation to ensure
data-informed decision-making at every level.
Job Description The Machine Learning Engineer is responsible for building, deploying, and continuously
improving RTV’s production LLM applications, which are currently live across multiple platforms
and actively used by field teams and program staff across Uganda, Rwanda, and the
Democratic Republic of Congo. The role sits within the Predictive Analytics / VENN department
and focuses on advancing agentic LLM architectures, RAG systems, and evaluation
infrastructure as RTV scales its AI capabilities to new countries and deepens integration with
mobile field tools and the data warehouse. A core area of responsibility is the SBCC (Social and
Behavior Change Communication) system, which generates personalized, practice-specific
behavior change messaging for field officers across agriculture, health, livestock, and
community 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 Software
Engineering team, and field program teams to deliver reliable, context-aware LLM applications
that integrate with RTV’s data warehouse, mobile implementation apps, and the broader
WorkMate AI ecosystem. This role also contributes to RTV’s strategic partnership with The
Agency Fund (TAF) AI Accelerator, supporting shared technical challenges in knowledge base
architecture, multi-country scaling, and LLM evaluation governance.
Key Responsibilities
Design and implement agentic LLM architectures including multi-step reasoning
pipelines, tool use, memory management, and autonomous workflow orchestration using
LangChain and related frameworks, applied across both conversational and generative
AI use cases.
Build, maintain, and optimize Retrieval-Augmented Generation (RAG) pipelines for
context-grounded LLM responses, including embedding strategy design, chunking
approaches, and retrieval optimization tailored to diverse content types such as program
documentation, household data, and behavioral practice guidelines.
Manage and evolve RTV’s vector database infrastructure (Chroma or Qdrant) including
index management, namespace organization, and multi-domain retrieval tuning to
support distinct organizational use cases.
Design, build, and maintain end-to-end ML pipelines covering data ingestion, feature
engineering, model training, evaluation, and deployment, ensuring reproducibility and
version control across all pipeline stages.
Apply knowledge of core ML algorithms — including supervised learning, classification,
regression, clustering, and neural network architectures — to select appropriate
modeling approaches for diverse problem types across RTV’s AI workstreams.
Develop and manage the full LLM application lifecycle — from prompt engineering and
chain construction through deployment, versioning, and production monitoring — using
LangChain 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, factual
grounding, cultural appropriateness, and content safety across generative outputs.
Instrument production LLM applications with LangSmith tracing, logging, and feedback
collection pipelines to enable continuous performance monitoring, failure analysis, and
iterative improvement cycles.
Build and deploy RESTful API endpoints for LLM-powered services, ensuring stable
integration with WorkMate and the RTV mobile implementation app used by field officers
during household visits.
Develop and maintain personalized content generation pipelines that leverage
household segmentation, behavioral data, and program-specific context from the data
warehouse to produce targeted, practice-specific outputs at scale.
Implement offline and low-connectivity strategies including message caching and
fallback mechanisms to ensure AI-powered tools remain accessible to field officers in
remote locations.
Collaborate with the Applied Learning team to incorporate validated program content into
knowledge bases and generation templates, ensuring evidence-based alignment and
content quality across all LLM outputs.
Write clear technical documentation for agent architectures, RAG pipeline designs,
evaluation frameworks, and API specifications to support team collaboration and
organizational 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, and
RAG implementation.
○ LangSmith for LLM application tracing, evaluation, dataset management, and
production monitoring.
○ Vector databases (Chroma and/or Qdrant) including embedding management,
indexing, and retrieval optimization.
○ Agentic design patterns including ReAct, plan-and-execute, multi-agent
orchestration, and tool-augmented reasoning.
○ LLM evaluation methodologies including LLM-as-a-judge frameworks,
reference-based and reference-free metrics, and human-in-the-loop evaluation
workflows.
○ Python for LLM application development, API construction (FastAPI or
equivalent), 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 unsupervised
learning, classification, regression, ensemble methods, and neural network
architectures, with the ability to select and apply appropriate approaches for varied
problem types.
Hands-on experience building and managing ML pipelines including data preprocessing,
feature engineering, model training, evaluation, experiment tracking (Weights & Biases
or equivalent), and production deployment using CI/CD practices.
Familiarity with mobile application integration and offline-first design patterns for
low-connectivity deployment environments is an asset.
Personal Attributes
Genuine commitment to using AI for social impact and poverty alleviation in last-mile
communities.
Strong engineering discipline with attention to reliability, safety, and cultural sensitivity in
AI-generated content.
Ability to translate complex LLM system outputs into accessible insights for non-technical
field staff and program managers.
Collaborative and communicative team player who can work across analytics, software
development, and field program teams.
High degree of ownership, intellectual curiosity, and drive to stay current with the
fast-moving LLM engineering landscape.
Raising The Village is committed to Equity and Inclusion in the workplace and is proud to be an
equal opportunity employer
Application procedure
CLICK HERE TO APPLY
Posting Date: 2026-03-13
