Must have

• AWS

• Azure

• ML

• PyTorch

• TensorFlow

• scikit-learn

• MLOps

• MLflow

• Kubeflow

• SageMaker

• LLMs

• English

Requirements description

• Master’s degree in Computer Science, Machine Learning, or a closely related technical field
• 5+ years of hands-on experience in developing and deploying machine learning systems in real-world production environments
• Strong background with cloud platforms (e.g., AWS, Azure), including services for data processing, training, and model deployment
• Proficient in Python and commonly used ML frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn)
• Deep understanding of machine learning system architecture, including data pipelines, model lifecycle management, and performance optimization
• Experience with MLOps tools and best practices (e.g., MLflow, Kubeflow, SageMaker)
• Familiarity with LLMs, prompt engineering, and inference optimization in the context of generative AI
• Solid software engineering fundamentals, including object-oriented programming, version control, testing, and writing clean, maintainable code
• Ability to work both independently and collaboratively within a fast-paced, cross-functional team environment

Offer description

About the Role:

We are looking for a Senior AI Engineer to join a forward-thinking team focused on developing and deploying cutting-edge machine learning solutions. In this position, you will be responsible for designing, implementing, and scaling AI systems that improve engineering processes and enhance operational performance across a global automotive environment.

This role offers the opportunity to work on real-world challenges by developing robust, cloud-native ML infrastructure and applying state-of-the-art AI technologies to drive innovation within engineering and operations.

Your responsibilities

1. Analyze technical and business requirements to architect effective AI/ML solutions
2. Develop and deploy production-ready machine learning models, ensuring they meet standards for scalability, performance, and maintainability
3. Explore and evaluate new algorithms and techniques—including Large Language Models (LLMs) and Generative AI approaches—for integration into practical systems
4. Build and maintain end-to-end data and model pipelines, including data ingestion, training, validation, and model serving
5. Continuously monitor deployed systems for performance and reliability, and iterate for improved business outcomes
6. Support the development and adoption of MLOps practices such as reproducibility, CI/CD for ML workflows, and automated monitoring

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