Senior Analytics & MLOps Platform Engineer

Posted 2 months agoViewed

💎 Seniority level: Senior

📍 Location: UK, Europe, Africa, UTC -1, UTC+3

🔍 Industry: Digital and financial inclusion

🗣️ Languages: English

🪄 Skills: PythonApache AirflowKubernetesMachine LearningAzureData engineeringCI/CDTerraform

Experience managing ML infrastructure in production.
Proficient with infrastructure-as-code tools like Azure Bicep, Terraform, ARM, or CloudFormation.
Experience with Kubernetes or platforms for containerized applications.
Knowledge of orchestration systems such as Apache Airflow.
Proficiency in programming languages such as Python, C#, or Java.
Certification in Azure Solutions Architect Expert or similar.

Design and implement architectures for exploration, training, deployment, and monitoring of ML models.
Build and maintain CI/CD pipelines for ML models, ensuring scalability and reliability.
Implement version control for models and feature sets for reproducibility and compliance.
Automate and manage infrastructure for data pipelines and ML model training using Azure and infrastructure-as-code tools.
Establish engineering patterns for feature engineering and model reuse.
Develop workflows for model validation, testing, and deployment integrated with CI/CD systems.

Posted 8 days ago

📍 UK, Europe, Africa

🧭 Full-Time

🔍 Fintech

🏢 Company: M-KOPA

🔧 Requirements

Experience managing machine learning infrastructure in production
Working with infrastructure-as-code tools such as Azure Bicep, Terraform, ARM, CloudFormation or similar
Good practical experience in data engineering
Experience with Kubernetes or other platforms for containerized applications
Working with orchestration systems such as Apache Airflow
Proficiency in programming languages (Python, C#, Java, etc.)
Certification in Azure Solutions Architect Expert or similar

💡 Responsibilities

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.
Implementing version control for models and feature sets to ensure reproducibility, traceability, and compliance with best practices.
Automating and managing infrastructure for data pipelines, machine learning model training, and serving using Azure and infrastructure-as-code tools.
Establishing 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.

PythonApache AirflowKubernetesMachine LearningMLFlowAzureData engineeringCI/CDTerraformData analytics

Posted 8 days ago