LS Solutions logo

AI Machine Learning Engineer

LS Solutions

HybridRaymond, OHseniorPosted 1d ago

Job description

Role Overview:

Lead the design, development, and deployment of advanced AI and machine learning solutions for automotive R&D, focusing on production-grade AI for vehicle development, simulation, manufacturing quality, and digital twins. Mentor engineers and collaborate with CAE, CAD, manufacturing, and data platform teams to deliver end-to-end AI solutions.

Key Responsibilities

  • Develop and validate impactful AI/ML solutions for automotive engineering and manufacturing use cases.

  • Design and implement AI surrogate models using Graph Convolutional Neural Networks (GCNNs) to augment physics-based CAE.

  • Architect and deploy scalable cloud-based AI systems on AWS/Azure, ensuring compliance with enterprise governance.

  • Manage the full AI lifecycle: data ingestion, feature engineering, training, deployment, and monitoring.

  • Implement MLOps and GenAIOps best practices, including versioning, drift detection, CI/CD, and traceability.

  • Create agentic AI solutions for CAE workflows and support ETL activities related to ADC data.

  • Establish design standards, ensure code quality, and document processes for reuse and auditability.

  • 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 of experience developing and deploying ML/AI systems; 3+ years in production environments.

  • Hands-on expertise with graph neural networks (GCNNs, GNNs), advanced Python skills, and familiarity with C++/Java.

  • Proficiency with ML frameworks like PyTorch, TensorFlow, scikit-learn.

  • Experience deploying AI on cloud platforms (AWS/Azure), with knowledge of containers and MLOps tools.

  • Strong foundation in statistics, optimization, and numerical methods; experience with CAE or physics-informed ML is a plus.

  • Prior automotive or engineering experience is advantageous.

Work Environment:

Primarily office-based with potential hybrid options; occasional travel and overtime may be required to support project milestones.