Lead AI for Design and Control

07 ago - Italia
Macchinari e impiantistica

Lead AI for Design and Control
Our team provides industry-leading products and services that optimize the production and processing of energy. You will contribute to our future as an AI player in the Energy Transformation industry. We implement and adapt AI algorithms across a broad variety of projects. These include predictive maintenance and defect detection, control and robotics, design optimization and simulation, unmanned inspections and many others. Our team deploys production-ready code in cloud and edge environments to make energy products more efficient, reliable, safe and sustainable.
Responsibilities
Collaborating with multidisciplinary teams in the definition of new AI‐powered engineering workflows for product optimization and simulation (in terms of performance, manufacturability, controllability)
Leading technical development of research and development projects on the combination of AI and engineering methodologies
Developing and integrating software components for AI applications according to customer and technical requirements




Checking availability and relevance of internal and external data sources
Proposing, and leading new data collection activities. Cleaning and validating data
Working closely with AI Product Owners to define project technical goals and requirements
Collaborating with AI Engineers to define the project software architecture and maintenance/retraining strategy
Working with Engineering Subject Matter Experts to adapt AI technology to industry needs
Qualifications
Have experience in defining the technical requirements and expectations of AI solutions for engineering applications
Have deep understanding of machine learning and AI algorithms for regression, classification and anomaly detection tasks applied to tabular data with uncertainty quantification and explainability requirements
Have proven experience applying AI algorithms for dynamic data sources (time series) regression, clustering, anomaly detection and uncertainty quantif

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