Job DescriptionAbout 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 solutionsDevelop and deploy production-ready machine learning models, ensuring they meet standards for scalability, performance, and maintainabilityExplore and evaluate new algorithms and techniques—including Large Language Models (LLMs) and Generative AI approaches—for integration into practical systemsBuild and maintain end-to-end data and model pipelines, including data ingestion, training, validation, and model servingContinuously monitor deployed systems for performance and reliability, and iterate for improved business outcomesSupport the development and adoption of MLOps practices such as reproducibility, CI/CD for ML workflows, and automated monitoringWork closely with data scientists, software engineers, and other ML professionals to deliver impactful, high-quality AI solutionsStay informed on emerging research and developments in the AI/ML landscape, with a focus on generative models and their practical applicationsQualifications:Master’s degree in Computer Science, Machine Learning, or a closely related technical field5+ years of hands-on experience in developing and deploying machine learning systems in real-world production environmentsStrong background with cloud platforms (e.g., AWS, Azure), including services for data processing, training, and model deploymentProficient 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 optimizationExperience with MLOps tools and best practices (e.g., MLflow, Kubeflow, SageMaker)Familiarity with LLMs, prompt engineering, and inference optimization in the context of generative AISolid software engineering fundamentals, including object-oriented programming, version control, testing, and writing clean, maintainable codeAbility to work both independently and collaboratively within a fast-paced, cross-functional team environment