Senior Backend Software Engineer - AI Factory - F/M/NB Cegid is a European leader in cloud business management solutions for finance (cash-flow, tax, ERP), human resources (payroll, talent management), CPAs, retail, and entrepreneurial sectors. In today's rapidly changing world, Cegid & its 4,400 employees make more possible by helping their 500,000 customers unleash their potential thanks to innovative and purposeful business solutions.
Make more possible , is our vocation. It reflects who we are, how and why we do things the way we do them for our clients. Thanks to this, we can affirm that we work every day to shape your future , ours and our clients' industries' future. A future we have been defining for years with our employees, by inventing solutions that change the way people work, for sustainable performance.
@AI Factory
The AI Factory is dedicated to advancing our management software through AI innovation . Our primary goal is to seamlessly integrate AI features into our product line, either by enhancing functionality and user experience or by recreating user workflows to take full advantage of AI technology.
What will you achieve?
As a Backend Engineer on this AI Team, you will play a crucial role in designing, implementing, and maintaining the backend infrastructure that supports our innovative Generative AI projects. You will work collaboratively with a team of skilled professionals to build scalable and efficient systems that facilitate the development and deployment of advanced AI models.
As a Backend Engineer, you will:
Spearhead the creation and ongoing enhancement of micro-services essential for Generative AI applications, ensuring they work harmoniously with front-end interfaces through team collaboration.
Execute containerization with Docker and manage deployments using Kubernetes to guarantee system scalability and dependability.
Contribute to the architecture of robust data management frameworks for large-scale data crucial to Generative AI , and streamline data flows in partnership with data scientists, applying MLOps methodologies to effectively deploy, monitor, and maintain AI models.
Leverage Microsoft Azure cloud services to optimize and scale Backend systems for high performance.
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