Engineer

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Skills you'll use

What you'll do

• Design, develop, and optimize AI/ML models for automotive use cases such as driver monitoring, predictive analytics, perception, diagnostics, or connected vehicle applications.
• Build and maintain data pipelines for data collection, preprocessing, transformation, validation, and feature engineering from structured and unstructured sources.
• Work on end-to-end model lifecycle activities including training, evaluation, deployment, versioning, and performance monitoring.
• Collaborate with software, data, validation, and platform teams to integrate AI/ML components into production systems.
• Support deployment of AI/ML workloads using DevOps/MLOps practices, including CI/CD, containerization, automated testing, and infrastructure management.
• Develop and maintain scripts, APIs, and services for scalable model serving and batch/stream processing.
• Contribute to developer productivity initiatives by leveraging AI tools for code review, code generation, documentation, test-case generation, defect analysis, and workflow automation.
• Evaluate and integrate AI-assisted engineering tools to improve software development speed, code quality, and release efficiency.
• Ensure data quality, reproducibility, and traceability across datasets, code, and model artifacts.
• Participate in troubleshooting, root-cause analysis, and continuous improvement of deployed AI/ML solutions.

Who can apply

• Around 4 years of experience in AI/ML engineering, preferably in the automotive domain.
• Strong programming skills in Python.
• Good understanding of machine learning and deep learning concepts, including model training, validation, and inference workflows.
• Hands-on experience in building and maintaining data pipelines using tools/frameworks such as Spark, Airflow, Kafka, or similar.
• Exposure to DevOps/MLOps practices, including Docker, Kubernetes, CI/CD pipelines, Git, and cloud/on-prem deployment workflows.
• Experience with data preprocessing, feature engineering, model evaluation, and debugging.
• Familiarity with APIs, microservices, and deployment of AI/ML solutions into production environments.
• Good understanding of software engineering best practices, version control, testing, and documentation.
• Competencies Required
• Strong problem-solving and analytical skills.

About Stellantis

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