AI Machine Learning Engineer

About the role

Lead the design, development, and deployment of advanced AI and machine learning solutions to support automotive R&D initiatives. Focus on production-grade AI for vehicle development, simulation, manufacturing quality, and digital twins, owning solutions end-to-end and mentoring engineers. Collaborate with CAE, CAD, manufacturing, and data platform teams. Primarily office work with possible hybrid arrangements. Occasional travel and overtime may be needed. Work involves collaboration across engineering, simulation, and manufacturing teams.

Responsibilities

• Develop and validate impactful AI/ML solutions for automotive engineering and manufacturing.
• Design and deploy AI surrogate models, such as Graph Convolutional Neural Networks (GCNNs), to augment or replace physics-based CAE models.
• Architect scalable cloud-based AI systems on AWS/Azure, ensuring enterprise compliance.
• Manage the full AI lifecycle: data ingestion, feature engineering, training, evaluation, deployment, and monitoring.
• Implement MLOps and GenAIOps practices, including versioning, drift detection, CI/CD, and traceability.
• Develop agentic AI solutions for CAE workflows and deploy AI agents for execution and monitoring.
• Support ETL activities related to CAE data structure and establish design standards for code quality and documentation.
• Mentor and guide junior engineers in AI development best practices.

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field, or equivalent experience.
• 8+ years of experience in developing and deploying ML/AI systems; 3+ years in production environments.

Skills

• Hands-on experience with graph neural networks (GCNNs, GNNs, GATs, MPNNs).
• Proficiency in Python; C++ or Java is a plus for automotive contexts.
• Expertise in ML frameworks like PyTorch, TensorFlow, scikit-learn.
• Strong foundation in statistics, optimization, and numerical methods.
• Experience deploying AI solutions on cloud platforms (AWS/Azure), including containerization and MLOps tools.
• Knowledge of model governance, responsible AI, and simulation/CAE/physics-informed ML is preferred.