AI Engineer

Overview

End-to-end AI responsibilities : Design, build, and scale advanced AI solutions. Have a broad understanding of different computer vision and text processing-oriented architectures and their applicability. Own the full lifecycle of data science initiatives, from ideation and data ingestion to production-grade model deployment. Drive continuous experimentation to optimize model performance and deliver measurable business impact. Strategic collaboration and stakeholder alignment. Partner with business leaders to translate needs into clear project scopes, objectives, and success metrics. Define technical and business success criteria with project managers and stakeholders. Ensure solutions meet corporate standards for data quality, security, privacy, and regulatory compliance (including EU AI Act and regional laws). Technical mentorship : Mentoring junior data scientists through practical guidance, skills gap training, and joint solution development. Foster a culture of excellence, collaboration, and continuous learning within the data science team. Innovation and future-ready AI : Stay ahead of the curve in machine learning and AI advancements. Proactively identify business challenges and propose high-impact AI innovation projects.

Responsibilities

• Design, build, and scale advanced AI solutions and understand applicable architectures.
• Own the full lifecycle of data science initiatives, from ideation and data ingestion to production-grade model deployment.
• Drive continuous experimentation to optimize model performance and deliver measurable business impact.
• Strategic collaboration and stakeholder alignment.
• Partner with business leaders to translate needs into clear project scopes, objectives, and success metrics.
• Define technical and business success criteria with project managers and stakeholders.
• Ensure solutions meet corporate standards for data quality, security, privacy, and regulatory compliance (including EU AI Act and regional laws).
• Technical mentorship : mentoring junior data scientists through practical guidance, skills gap training, and joint solution development.
• Foster a culture of excellence, collaboration, and continuous learning within the data science team.
• Innovation and future-ready AI : stay ahead of the curve in machine learning and AI advancements and proactively identify business challenges and high-impact AI innovation projects.

Requirements

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