Autonomous Driving Feature Development Engineer

Aptiv•Carmel, IN

About The Position

Autonomous Driving Feature Development Engineer Aptiv is a global technology company that develops safer, greener and more connected solutions enabling the future of mobility. Headquartered in Dublin, Aptiv has approximately 180,000+ employees and operates 12 technical centers, as well as manufacturing sites and customer support centers in 44 countries. Visit aptiv.com. Please review Aptiv's privacy policy by following this link: https://www.aptiv.com/privacy-notice

Responsibilities

Develop and evaluate machine learning models for various AI applications, leveraging techniques such as supervised learning, unsupervised learning, and reinforcement learning.
Conduct thorough model evaluation and validation, including performance metrics analysis, to ensure accuracy, reliability, and robustness.
Implement and optimize automated machine learning (AutoML) pipelines and frameworks to streamline model development, training, and deployment processes.
Utilize AutoML tools and techniques to automate feature engineering, model selection, and hyperparameter tuning for improved efficiency and scalability.
Utilize cloud computing platforms such as AWS, Azure, or Google Cloud to deploy and scale machine learning models and applications.
Leverage cloud-based services and resources for data storage, processing, and analysis to support AI/ML workflows and pipelines.
Implement continuous integration and continuous development (CICD) pipelines and practices to automate model deployment, testing, and monitoring.
Integrate AI/ML solutions into existing CICD workflows, ensuring seamless integration with software development processes.
Apply knowledge of automotive product development processes and industry standards to design and develop AI/ML solutions for automotive applications.
Collaborate with cross-functional teams to understand product requirements, specifications, and constraints, ensuring alignment with automotive development practices.
Design and conduct A/B tests and experiments to evaluate the effectiveness and impact of AI/ML models and algorithms in real-world scenarios.
Analyze test results and make data-driven recommendations for model improvements and optimizations based on observed performance metrics.
Develop and orchestrate ML workflows using Kubeflow Pipelines to automate model training, evaluation, and deployment processes in Kubernetes environments.
Implement continuous integration and delivery pipelines using Jenkins to automate build, test, and deployment tasks for AI/ML projects.