Machine Learning Engineer, Consultant

Irembo is a technology company that designs and develops digital products to ease the accessibility of services in users’ everyday lives worldwide, starting with Rwanda. Our pioneer products, IremboGov and IremboPay, have enabled Rwandan citizens and foreigners to access and pay for over 150 public services online through our one-stop-shop e-governance and payment platforms. To date, we have facilitated over 30 million transactions through our platforms and have ambitious goals to scale our technology worldwide to enable more governments and institutions to serve their citizens better. More information is available on irembo.com.

Location: Kigali, Rwanda, or Remote.

Duration: 10 months.

Terms of Reference - Machine Learning Engineer, Consultant

The Opportunity 

We are looking for a skilled and dedicated Machine Learning Engineer to join our team on a focused 10-month contract. You will be essential in the end-to-end Machine Learning lifecycle for our AI solutions, which include supporting citizens in applying for government services and implementing advanced Voice over AI capabilities. This role requires expertise in building new models from scratch, refining existing ones, and establishing robust training and deployment pipelines.

Key Responsibilities: 

I. Model Design, Development, and Training

• New Model Creation: Design, develop, and implement novel machine learning models and algorithms (e.g., using Deep Learning, NLP, or statistical techniques) tailored to specific challenges within the government services application process and the Voice over AI feature.
• Model Refinement & Optimization: Refine, customize, and fine-tune existing pre-trained or proprietary models for increased accuracy, reduced latency, and generalizability, particularly for Natural Language Understanding (NLU) components.
• Experimentation: Plan, manage, and execute model training runs and experiments at scale, including hyperparameter tuning, cross-validation, and rigorous performance evaluation.
• Data Pipelining: Collaborate with Data Engineers to build and maintain scalable data pipelines for ingestion, cleaning, feature engineering, and labeling of high-quality data used for model training.

II. Deployment and MLOps Infrastructure

• Production Deployment: Engineer and deploy ML models into production environments, ensuring they are robust, scalable, and meet performance SLAs for real-time inference, especially for the Voice over AI application.
• MLOps Implementation: Develop and maintain MLOps infrastructure for model versioning, continuous integration (CI), continuous delivery (CD), and automated monitoring of model health and performance in production (e.g., drift detection, bias checking).
• Integration: Work with Software Engineers to integrate the model outputs via robust APIs (e.g., RESTful services) into the core software platform that citizens use.

III. Voice Over AI and NLP Focus

• Implement and optimize models specific to Natural Language Processing (NLP) and/or Speech Recognition/Synthesis for the core Voice over AI component.

IV. Technical Leadership and Strategy

• (Senior Focus) Provide technical guidance and mentorship to junior team members and help define the ML architecture and strategy for the project.
• Clearly articulate complex ML concepts, trade-offs, and results to both technical and non-technical stakeholders.
• Ensure all models and data processes comply with data privacy, security, and ethical AI standards relevant to public sector applications (e.g., fairness, explainability).

Qualifications: 

Required Skills & Experience

• Experience: Minimum of 3+ years of hands-on experience as an ML Engineer or AI Developer working on production systems.
• Programming & Frameworks: Expert proficiency in Python and deep experience with major ML/Deep Learning frameworks (e.g., TensorFlow, PyTorch).
• Domain Knowledge: Solid background in relevant AI domains, specifically Natural Language Processing (NLP) and/or Speech Recognition/Synthesis.
• Model Lifecycle: Proven ability to manage the entire ML lifecycle, from data preparation and feature engineering to training, optimization, and production deployment.
• Cloud/DevOps: Experience with MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes) for model scaling.
• Contract Focus: Demonstrated ability to work independently and deliver high-impact results under a tight, 10-month contract timeline.

Preferred Skills

• Master's degree or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
• Experience with Large Language Models (LLMs) and techniques like transfer learning or fine-tuning.
• Familiarity with compliance standards for government or public-sector technology.

Please note that the salary for this position is commensurate with experience and qualifications and will be discussed during the interview process. 

Application Deadline

• December 7, 2025

We are an equal opportunity employer and are committed to providing a positive interview experience for every candidate. We're on a mission to change our continent through technology and are committed to a diverse and inclusive workplace and strongly encourage applicants from all backgrounds, nationalities, and walks of life.

Our head office is based in Kigali, Rwanda.