AI Machine Learning Engineer

RoShay Services; Cincinnati (OH); Hybride; USD 150 000 - 190 000; Plein temps

Avantages offerts par ce poste

Tuition assistance

Wellbeing programs

Community engagement

Résumé du poste

RoShay Services seeks a seasoned AI/ML architect to lead design, development, and deployment of production-grade AI for vehicle R&D, simulation, and manufacturing quality. You will mentor engineers and collaborate with CAE, CAD, data teams to deliver end-to-end solutions.

Responsibilities include building GCNN-based surrogate models, scalable cloud deployments on AWS/Azure, and implementing MLOps with CI/CD. This role combines hands-on coding with technical leadership in a hybrid office setting.

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field.
• 8+ years in ML/AI, with 3+ in production environments.
• Hands-on experience with graph neural networks (GCNNs, GNNs, GATs, MPNNs) and advanced proficiency in Python; C++ or Java is a plus.
• Expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), cloud deployment (AWS/Azure), and MLOps tools (Docker, MLflow, CI/CD).
• Strong background in statistics, optimization, and numerical methods; experience with CAE/physics-informed ML is preferred.

Responsabilités

• Lead the design, development, and deployment of AI/ML solutions for automotive R&D, production-grade AI for vehicle development, simulation, manufacturing quality, and digital twins.
• Mentor engineers and collaborate with CAE, CAD, manufacturing, and data teams to deliver end-to-end AI solutions.
• Develop and validate AI/ML solutions with measurable impacts on automotive engineering and manufacturing.
• Design and deploy AI surrogate models using GCNNs to augment physics-based CAE.
• Architect scalable cloud-based AI systems on AWS/Azure, managing the full AI lifecycle from data ingestion to deployment and monitoring.
• Implement MLOps and GenAIOps practices, including versioning, drift detection, and CI/CD.
• Create agentic AI solutions and deploy AI agents for workflow execution and monitoring.
• Support data activities related to CAE structures and establish standards for code quality and documentation.
• Mentor junior engineers and guide technical development.

Connaissances

GCNNs

GNNs

GATs

MPNNs

Python

C++/Java

PyTorch

TensorFlow

scikit-learn

AWS

Azure

Docker

MLflow

CI/CD

Statistics

Optimization

Numerical methods

Physics-informed ML

Formation

Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field

Outils

AWS

Azure

Docker

MLflow

CI/CD

Description du poste

Role Overview:

Lead the design, development, and deployment of advanced AI and machine learning solutions supporting 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 teams to deliver end-to-end AI solutions.

Key Responsibilities

• Develop and validate AI/ML solutions with measurable impacts on automotive engineering and manufacturing.
• Design and deploy AI surrogate models using Graph Convolutional Neural Networks (GCNNs) to augment physics-based CAE.
• Architect scalable cloud-based AI systems on AWS/Azure, managing the full AI lifecycle from data ingestion to deployment and monitoring.
• Implement MLOps and GenAIOps practices, including versioning, drift detection, and CI/CD.
• Create agentic AI solutions and deploy AI agents for workflow execution and monitoring.
• Support data activities related to CAE structures and establish standards for code quality and documentation.
• Mentor junior engineers and guide technical development.

Qualifications & Skills

• Bachelor’s or Master’s in Computer Science, Engineering, Data Science, or related field; 8+ years in ML/AI, with 3+ in production environments.
• Hands-on experience with graph neural networks (GCNNs, GNNs, GATs, MPNNs) and advanced proficiency in Python; C++ or Java is a plus.
• Expertise in ML frameworks (PyTorch, TensorFlow, scikit-learn), cloud deployment (AWS/Azure), and MLOps tools (Docker, MLflow, CI/CD).
• Strong background in statistics, optimization, and numerical methods; experience with CAE/physics-informed ML is preferred.

Work Environment & Benefits

Primarily office-based with hybrid options, supporting cross-functional teams. Offers competitive salary, benefits, career growth opportunities, and additional perks like tuition assistance, wellbeing programs, and community engagement.