AI Machine Learning Principal Engineer

Sotalent; Raymond (OH); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Hybrid office/home

Relocation assistance

Education reimbursement

Tuition assistance

Disability insurance

401(k) with match

Company car programme

Job summary

Sotalent in the United States is seeking an AI Machine Learning Principal Engineer to lead end-to-end AI/ML initiatives across automotive engineering, simulation, manufacturing quality and digital twin domains.

You will mentor engineers, shape design standards and collaborate with CAE, CAD and data platform teams to deliver production-grade AI solutions. Hybrid office/home setup may be available.

Qualifications

• 8+ years of experience developing and deploying AI/ML systems.
• 3+ years in production environments.
• Hands-on with Graph Neural Networks (GCNNs, GNNs, GATs, MPNNs).
• Advanced proficiency in Python (and possibly C++/Java).
• Experience with ML frameworks: PyTorch, TensorFlow, scikit-learn.

Responsibilities

• Lead development and validation of AI/ML solutions for automotive engineering, CAE and manufacturing use cases.
• Design and deploy AI surrogate models using GCNNs to augment or replace physics-based CAE models.
• Architect and deploy scalable cloud-based AI systems using AWS and/or Azure.
• Own the full AI lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring.

Skills

GCNNs

Python

C++/Java

PyTorch

TensorFlow

scikit-learn

AWS/Azure

MLOps

Docker

CI/CD

LangGraph

LangChain

Strands SDK

Education

Bachelor's or Master's in CS/Engineering/Data Science

Tools

GNNs

Job description

A global automotive organisation is seeking an AI Machine Learning Principal Engineer to lead the design, development and deployment of advanced AI and machine learning solutions across automotive engineering, simulation, manufacturing quality and digital twin applications. The role owns AI solutions end-to-end while providing technical leadership and mentoring engineers across cross‑functional teams.

Key Responsibilities

• Lead the development and validation of AI/ML solutions for automotive engineering, CAE and manufacturing use cases.
• Design and deploy AI surrogate models using Graph Convolutional Neural Networks (GCNNs) to augment or replace physics‑based CAE models.
• Architect and deploy scalable cloud‑based AI systems using AWS and/or Azure.
• Own the full AI lifecycle, including data ingestion, feature engineering, model training, evaluation, deployment and monitoring.
• Implement MLOps and GenAIOps practices including versioning, drift detection, CI/CD and model traceability.
• Develop and deploy agentic AI solutions for CAE workflows in cloud environments.
• Build AI agents that execute, augment and monitor engineering workflows.
• Support ETL activities relating to CAE data structures.
• Establish design standards, coding practices and technical documentation to support reuse and auditability.
• Partner with CAE, CAD, manufacturing and data platform teams to deliver production‑grade AI solutions.
• Mentor and provide technical guidance to mid‑level and junior engineers.
• Contribute to the adoption of AI and machine learning capabilities across automotive R&D.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science or a related field, or equivalent experience.
• 8+ years of experience developing and deploying AI/ML systems, including 3+ years in production environments.
• Hands‑on experience with Graph Neural Networks, including GCNNs, GNNs, GATs or MPNNs.
• Advanced proficiency in Python.
• Experience with C++ or Java is a strong plus, particularly in automotive environments.
• Deep knowledge of ML frameworks including PyTorch, TensorFlow and scikit‑learn.
• Strong foundation in statistics, optimisation and numerical methods.
• Experience deploying AI solutions using AWS and/or Azure, including platforms such as Amazon Bedrock.
• Experience with LangGraph, LangChain and/or Strands SDK.
• Experience with containers, pipelines and MLOps tools such as Docker, MLflow and CI/CD.
• Knowledge of model governance, compliance and responsible AI frameworks.
• Experience with CAE, physics‑informed machine learning, surrogate modelling or simulation is preferred.
• Automotive or design engineering experience is a plus.
• Strong technical communication and collaboration skills.
• Ability to provide technical leadership and mentor other engineers.

Working Conditions

• Primarily office‑based work with hybrid office/home working potentially available.
• Occasional travel and overtime may be required depending on project milestones.
• Cross‑functional environment involving engineering, simulation, manufacturing and technology stakeholders.
• Competitive base salary based on location, experience and other factors.
• Regional bonus where applicable.
• Manager lease car programme, including vehicle, maintenance and insurance at no cost.
• Medical, dental, vision and prescription coverage.
• Paid vacation, holidays and company shutdown periods.
• Company‑paid short‑ and long‑term disability.
• 401(k) with company match and additional employer contribution.
• Relocation assistance where eligible.
• Education reimbursement and professional development programmes.
• Tuition assistance and student loan repayment support.
• Lifestyle, childcare and elder care support programmes.
• Community service and engagement programmes.