FullStack Developer - 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 5,000 employees make more possible by helping their 750,000 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 a 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 Full-Stack Software Engineer on our AI Team, you will play a crucial role in the development and deployment of our AI-driven solutions . You will work closely with our cross-functional team of researchers, data scientists, and software engineers to create innovative AI applications . Your expertise in both frontend and backend development will be essential in building robust and user-friendly AI-powered products.

As a Full-Stack Software Engineer, you will :

• Develop AI-Driven Applications : Design and develop full-stack applications with seamless integration of frontend and backend components using modern JavaScript frameworks (React / Angular / Vue) and backend technologies like .NET (C#) and Python.
• AI Integration and Optimization : Integrate AI / ML models, including NLP and Generative AI, into business applications and optimize performance for efficient AI interaction and scalability, ensuring best practices in security and clean architecture.
• API and Microservices Design : Design and implement APIs and microservices to expose AI capabilities efficiently, working with databases (SQL, NoSQL, vector databases) to support AI functionalities.
• Cloud Deployment and Monitoring : Deploy and monitor AI solutions in cloud environments (Azure) using containerization technologies such as Docker and Kubernetes.
• Collaborative Innovation : Collaborate with AI researchers, data scientists, DevOps, and product teams to deliver innovative AI-driven solutions, while staying updated with advancements in AI and software technologies.

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