AI Machine Learning Engineer

val's services; Raymond (OH); Hybrid; USD 140,000 - 200,000; Full time

Job summary

val's services seeks a senior AI/ML leader to drive end-to-end development for automotive R&D projects. You will design production-grade AI solutions, validate surrogate models like GCNNs, and deploy scalable cloud systems on AWS/Azure while ensuring governance and compliance.

You will mentor engineers, manage the full lifecycle from data to deployment, and collaborate across CAE, CAD, manufacturing, and data platforms to deliver impactful results.

Qualifications

• Bachelor’s or Master’s in CS/Engineering/Data Science or related field.
• 8+ years developing ML/AI systems; 3+ years in production environments.
• Hands-on experience with graph neural networks (GCNNs, GNNs, GATs, MPNNs).
• Proficiency in Python; C++ or Java 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.

Responsibilities

• Lead design, development, and deployment of AI/ML solutions for automotive R&D.
• Design and deploy AI surrogate models (GCNNs) to augment physics-based CAE models.
• Architect scalable cloud-based AI systems on AWS/Azure with enterprise compliance.
• Manage AI lifecycle: data ingestion, feature engineering, training, deployment, monitoring.
• Implement MLOps and GenAIOps, including versioning, drift detection, CI/CD, 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.

Skills

GCNNs

GNNs

GATs

MPNNs

Python

PyTorch

TensorFlow

scikit-learn

C++

Education

Bachelor’s or Master’s in CS/Engineering/Data Science or related field

Tools

AWS

Azure

Docker

Kubernetes

Job description

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, owning solutions end-to-end and mentoring engineers. Collaborate with CAE, CAD, manufacturing, and data platform teams.

Key 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 & Skills

• 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.
• 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.

Work Environment & Additional Info:

Primarily office work with possible hybrid arrangements. Occasional travel and overtime may be needed. Work involves collaboration across engineering, simulation, and manufacturing teams.