Vista previa generada por IA
Machine Learning Engineer Expert – design, deploy, and optimize large-scale AI models on Azure, based in Sala El Jadida, Morocco.
Lead MLOps initiatives, build CI/CD pipelines, collaborate across data science, engineering, and DevOps teams in an agile environment.
Overview
As an experienced Machine Learning Engineer, you will be responsible for designing, developing, deploying, and optimizing large-scale AI models to meet business needs. You will play a key role in establishing a robust, scalable MLOps architecture, ensuring high performance, reliability, and maintainability of production solutions on Azure cloud.
Key Responsibilities
Model Design and Development: Design, train, and optimize Machine Learning and Deep Learning models using frameworks such as TensorFlow, PyTorch, and Scikit-learn. Collaborate with Data Scientists to turn prototypes into production-ready solutions.
Industrialization and Deployment: Implement CI/CD pipelines for training, evaluation, and deployment of models on Azure. Automate these processes to ensure continuous, reliable delivery.
Performance Optimization in Production: Improve model inference performance, reduce latency, and optimize costs. Make adjustments to ensure scalability and robustness.
MLOps and Cloud Architecture: Contribute to building a comprehensive MLOps architecture, including versioning data and models, model registry, monitoring, and incident management.
Documentation and Best Practices: Document models, pipelines, and processes to ensure maintainability, reusability, and compliance with company standards.
Collaboration and Communication: Work closely with Data Science, Data Engineering, and DevOps teams in an agile, multicultural environment to deliver high-value solutions.
Technical Skills Required
Programming Languages: Python, SQL, PySpark
ML Frameworks and Tools: TensorFlow, PyTorch, Scikit-learn, MLflow, Kubeflow
Cloud Platforms: Azure (Azure ML, AKS, Data Lake, Data Factory, Databricks)
DevOps & Automation: Docker, GitHub Actions, Azure DevOps, Terraform (preferred)
Distributed Architecture: Strong understanding of distributed systems, data/model versioning, and scalable deployment practices
Experience
Minimum of 5 years in Machine Learning, Data Engineering, or related fields
Proven experience in end-to-end model deployment, monitoring, and maintenance in production
Cloud experience, ideally with Azure, for implementing MLOps solutions
Soft Skills
Analytical mindset with strong technical rigor
Excellent communication and collaboration skills
Ability to work in agile, multicultural environments, taking ownership of projects
Delivery-oriented with a focus on ownership and results