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A Principal AI Engineer has complete oversight of AI Engineering within a platform. Individuals in this role leverage their extensive proficiency of machine learning models, natural language processing, computer vision, and other AI technologies to set direction, define strategy and architectural best practices, build organizational capability, and champion the role of AI Engineering in driving business priorities.
Preferred:
A Principal AI Engineer has complete oversight of AI Engineering within a platform. Individuals in this role leverage their extensive proficiency of machine learning models, natural language processing, computer vision, and other AI technologies to set direction, define strategy and architectural best practices, build organizational capability, and champion the role of AI Engineering in driving business priorities.
,[AI Solution Development, Drive the strategic vision for AI Engineering, leading cross-functional teams in the design, architecture and implementation of cutting-edge machine learning models and algorithms to tackle complex business challenges., Lead or oversee the development and deployment of scalable AI products, ensuring they align with organizational goals and deliver measurable value., Champion the use of state-of-the-art cloud technologies (e.g., AWS, Azure, Google Cloud) to enhance and promote the industrialization and operationalization of machine learning models and AI solutions across the organization., Provide expert guidance and mentorship on the operational support of machine learning models and algorithms, ensuring their reliability, performance, and scalability in production environments., Data Engineering, Lead the architecture and design of robust, scalable data pipelines in collaboration with data engineers, cloud engineers, and data scientists, enhancing data access and processing capabilities., Lead initiatives in data preprocessing, ingestion, and transformation activities across hybrid environments (both on-premises and cloud), ensuring adherence to best practices and organizational standards., Establish and promote comprehensive data quality assurance processes, including validation checks and data drift detection mechanisms, to ensure data consistency and integrity., Data Science, Mentor and guide team members in the model training process, focusing on industrialization through cloud technologies and achieving accuracy, reliability through experimentation and iterative improvements., Lead the evaluation and tuning of models, utilizing comprehensive metrics and performance benchmarks to refine algorithms and enhance predictive capabilities, ensuring alignment with business objectives., Integration and Deployment, Lead the integration of AI models into existing software systems and workflows, ensuring seamless functionality, performance, and optimal use Requirements: Python Additionally: Sport subscription, Private healthcare.