Senior AI Engineer

Job 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.

Key Responsibilities:

• Analyze technical and business requirements to architect effective AI/ML solutions

• Develop and deploy production-ready machine learning models, ensuring they meet standards for scalability, performance, and maintainability

• Explore and evaluate new algorithms and techniques—including Large Language Models (LLMs) and Generative AI approaches—for integration into practical systems

• Build and maintain end-to-end data and model pipelines, including data ingestion, training, validation, and model serving

• Continuously monitor deployed systems for performance and reliability, and iterate for improved business outcomes

• Support the development and adoption of MLOps practices such as reproducibility, CI/CD for ML workflows, and automated monitoring

• Work closely with data scientists, software engineers, and other ML professionals to deliver impactful, high-quality AI solutions

• Stay informed on emerging research and developments in the AI/ML landscape, with a focus on generative models and their practical applications

Qualifications:

• 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