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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.