AI Machine Learning Engineer

Role Overview: 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. Own solutions end-to-end, mentor engineers, and collaborate with CAE, CAD, manufacturing, and data teams.

Key Responsibilities

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

Qualifications & Skills

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field; or equivalent experience.
• 8+ years developing and deploying ML/AI systems; 3+ years in production environments.
• Hands-on experience with GCNNs, GNNs, GATs, MPNNs; proficiency in Python; experience with C++ or Java is a plus.
• Expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), cloud deployment (AWS/Azure), containers, MLOps tools, and responsible AI frameworks.
• Knowledge of CAE/physics-informed ML, surrogate modeling, or simulation is preferred.

Work Environment: Primarily office-based with hybrid options; occasional travel and overtime may be required. Collaborate across engineering, simulation, and manufacturing teams.