Machine Learning Engineer Expert

Job description

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