Senior Analytics And MLOps Platform Engineer

Summary

Join M-KOPA as a Senior Analytics & MLOps Platform Engineer and contribute to our mission of driving digital and financial inclusion. You will design, deploy, and manage data and machine learning infrastructure, enabling data scientists to build and deploy models. Responsibilities include building CI/CD pipelines, implementing model version control, and managing infrastructure using Azure and infrastructure-as-code tools. You will also develop workflows for model validation and deployment, optimizing for scalability and resource utilization. This is a fully remote role with flexible hours, offering opportunities for professional development and a supportive work environment. M-KOPA is a fast-growing company recognized for its impact and commitment to diversity and inclusion.

Requirements

• Have experience managing machine learning infrastructure in production
• Work with infrastructure-as-code tools such as Azure Bicep, Terraform, ARM, CloudFormation, or similar
• Have good practical experience in data engineering for machine learning or general analytics use cases
• Have experience with Kubernetes or other platforms for containerized applications
• Have experience working with orchestration systems such as Apache Airflow
• Have proficiency in programming languages (Python, C#, Java, etc.)

Responsibilities

• Design, deploy, and manage data and machine learning infrastructure to facilitate data scientists' experimentation, training, serving, and monitoring of batch and online machine learning models in production
• Design and implement architectures to streamline exploration, training, deployment, and monitoring of machine learning models
• Build and maintain CI/CD pipelines to deploy machine learning models into production, ensuring scalability, reliability, and continuous performance monitoring with automated retraining workflows
• Implement version control for models and feature sets to ensure reproducibility, traceability, and compliance with best practices
• Automate and manage infrastructure for data pipelines, machine learning model training, and serving using Azure and infrastructure-as-code tools (e.g., Azure Bicep, Terraform)
• Establish infrastructure and engineering patterns for feature engineering and reuse across a suite of models
• Develop workflows for model validation, testing, and deployment, fully integrated with CI/CD systems
• Enhance resource utilization to enable distributed processing and optimize workflows for scalability, including GPU/TPU acceleration

Preferred Qualifications

Have a certification in Azure Solutions Architect Expert or similar

Benefits

• Diverse development programs
• Coaching partnerships
• On-the-job training
• Family-friendly policies
• Prioritize well-being
• Embrace flexibility