Machine Learning Engineer Expert

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