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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
Utilize AutoML tools and techniques to automate feature engineering, model selection, and hyperparameter tuning for improved efficiency and scalability
Leverage cloud-based services and resources for data storage, processing, and analysis to support AI/ML workflows and pipelines
Integrate AI/ML solutions into existing CICD workflows, ensuring seamless integration with software development processes
Collaborate with cross-functional teams to understand product requirements, specifications, and constraints, ensuring alignment with automotive development practices
Analyze test results and make data-driven recommendations for model improvements and optimizations based on observed performance metrics
Implement continuous integration and delivery pipelines using Jenkins to automate build, test, and deployment tasks for AI/ML projects
Requirements:
Develop and evaluate machine learning models for various AI applications
Implement and optimize automated machine learning (AutoML) pipelines and frameworks
Utilize cloud computing platforms such as AWS, Azure, or Google Cloud
Implement continuous integration and continuous development (CICD) pipelines and practices
Apply knowledge of automotive product development processes and industry standards
Design and conduct A/B tests and experiments
Develop and orchestrate ML workflows using Kubeflow Pipelines
Implement continuous integration and delivery pipelines using Jenkins