Senior Engineer / Lead Engineer - Virtual Engineering - AI CFD

📋 Description

• • Drive innovation at the intersection of Artificial Intelligence (AI) and Computational Fluid Dynamics (CFD) as a Senior Engineer / Lead Engineer within General Motors' Virtual Engineering team.
• • Take ownership of end-to-end AI/ML projects, from conceptualization and development through to deployment and ongoing maintenance, addressing critical challenges in manufacturing engineering and process optimization.
• • Collaborate closely with cross-functional stakeholders to deeply understand complex business problems within the CFD domain, translating these needs into actionable AI-driven solutions.
• • Leverage API-based customization and advanced AI methodologies to develop robust and scalable solutions that enhance efficiency and predictive capabilities.
• • Spearhead the collection, meticulous cleaning, precise annotation, and preparation of diverse datasets, including text and image data, essential for advanced analysis and model training.
• • Design, develop, and rigorously fine-tune sophisticated AI/ML models tailored for specific CFD applications, including:
• • Automating the intricate processes of mesh generation, solver setup, and post-processing of complex CFD results, significantly reducing manual effort and turnaround times.
• • Constructing advanced optimization pipelines for thermal and fluid systems, employing AI-assisted approaches to achieve superior performance and efficiency.
• • Critically evaluate, validate, and benchmark the performance of developed AI models using a comprehensive suite of appropriate metrics, ensuring reliability and accuracy.
• • Seamlessly deploy validated AI models into production environments, working in close partnership with IT and dedicated AI teams to ensure smooth integration and operational readiness.
• • Establish and maintain robust monitoring and maintenance processes to guarantee sustained model accuracy and performance over time, adapting to evolving data and operational conditions.
• • Uphold the highest standards of data security, confidentiality, and regulatory compliance throughout the entire AI solution lifecycle, safeguarding sensitive organizational information.
• • Meticulously document all workflows, experimental results, and key lessons learned, contributing to a robust organizational knowledge base and fostering continuous learning.
• • Proactively stay abreast of the latest advancements and emerging trends in neural networks, multi-physics simulations, surrogate modeling, and physics-informed learning techniques, integrating cutting-edge research into practical applications.
• • Contribute to the strategic direction of AI adoption within the CFD domain, identifying new opportunities for innovation and impact.
• • Mentor junior engineers and contribute to the growth of the AI/CFD competency within the organization.
• • Champion the adoption of AI-driven methodologies to solve challenging engineering problems, driving tangible improvements in product development and manufacturing processes.
• • Ensure that AI solutions are not only technically sound but also aligned with business objectives and deliver measurable impact.
• • Foster a culture of continuous improvement and innovation within the team, encouraging experimentation and the exploration of novel AI techniques.
• • Translate complex fluid dynamics phenomena into data-driven insights and predictive models.
• • Develop and implement strategies for efficient data management and feature engineering from large-scale CFD datasets, extracting meaningful insights from flow fields like velocity, pressure, and turbulence quantities.
• • Apply a strong problem-solving and analytical mindset to tackle multifaceted engineering challenges.
• • Understand and utilize data annotation tools and MLOps workflows to streamline the AI development lifecycle.
• • Gain practical experience in domain-specific AI use cases within the automotive manufacturing sector.
• • Contribute to the development of next-generation simulation tools and methodologies that leverage AI for enhanced speed and accuracy.
• • Collaborate with simulation experts to validate AI model predictions against traditional CFD results.
• • Ensure the scalability and robustness of AI solutions for large-scale industrial applications.
• • Communicate technical findings and recommendations effectively to both technical and non-technical audiences.