We are looking for a Senior Analytics & MLOps Platform Engineer to join our Analytics team; as we scale up and drive digital and financial inclusion across our markets.
As a Senior Analytics & MLOps Platform Engineer, you will be designing, deploying and managing data and machine learning infrastructure to make it easy for data scientists to experiment, train, serve and monitor batch and online machine learning models in production.
About Role
Our mission is to improve the ability of Product, Operations and Data Science teams in M-KOPA to make data driven decisions and automate them using machine learning.
You will be designing and implementing architectures to streamline exploration, training, deployment and monitoring of machine learning models. Building and maintain CI/CD pipelines to deploy machine learning models into production, ensuring scalability, reliability, and continuous performance monitoring with automated retraining workflows.
You will also implement version control for models and feature sets to ensure reproducibility, traceability, and compliance with best practices. Using Azure and infrastructure-as-code tools (e.g., Azure Bicep, Terraform) you will automate and manage infrastructure for data pipelines, machine learning model training, and serving.
Additionally, you will establish infrastructure and engineering patterns to feature engineering and reuse across suite of models. Developing workflows for model validation, testing, and deployment, fully integrated with CI/CD systems, while enhancing resource utilization, to enable distributed processing, and optimize workflows for scalability, including GPU/TPU acceleration.
This is a fully remote role, you would be working within the following time zone (UTC -1 / UTC+3) with a diverse group of other employees working remotely from locations such as UK, Europe and Africa. You will be reporting to the Head of Analytics at M-KOPA.
Expertise
Our expectation is that you have experience managing machine learning infrastructure in production, working with infrastructure-as-code tools such as Azure Bicep, Terraform, ARM, CloudFormation or similar, and good practical experience in data engineering, for machine learning or general analytics use case.
Additionally, having experience with Kubernetes or other platforms for containerized applications as well as working with orchestration systems such as Apache Airflow would be essential to succeed in this role.
The ideal candidate for this role would need to have proficiency in programming languages (Python, C#, Java, etc.) as well as a certification in Azure Solutions Architect Expert or similar.
Why M-KOPA?
At M-KOPA, we empower our people to own their careers through diverse development programs, coaching partnerships, and on-the-job training. We support individual journeys with family-friendly policies, prioritize well-being, and embrace flexibility.
Join us in shaping the future of M-KOPA as we grow together. Explore more at m-kopa.com.
Recognized thrice by the Financial Times as one Africa's fastest growing companies (2022, 2023 and 2024) and by TIME100 Most influential companies in the world 2023 and 2024 , we've served over 5 million customers, unlocking $1.5 billion in cumulative credit for the unbanked across Africa.
Important Notice
M-KOPA is an equal opportunity and affirmative action employer committed to assembling a diverse, broadly trained staff. Women, minorities, and people with disabilities are strongly encouraged to apply.
M-KOPA explicitly prohibits the use of Forced or Child Labour and respects the rights of its employees to agree to terms and conditions of employment voluntarily, without coercion, and freely terminate their employment on appropriate notice. M-KOPA shall ensure that its Employees are of legal working age and shall comply with local laws for youth employment or student work, such as internships or apprenticeships.
M-KOPA does not collect/charge any money as a pre-employment or post-employment requirement. This means that we never ask for ‘recruitment fees’, ‘processing fees’, ‘interview fees’, or any other kind of money in exchange for offer letters or interviews at any time during the hiring process.
