Senior Analytics & MLOps Platform Engineer at M-KOPA

• 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.