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