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
6,000 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 Team
The Generative AI Engineer designs, builds, evaluates, and deploys production-ready AI-powered applications. This role combines applied machine learning, generative AI, backend engineering, and cloud deployment skills to turn AI capabilities into scalable, secure, and maintainable business solutions. The role focuses on large language models, retrieval-augmented generation, AI agents, workflow automation, APIs, and enterprise AI services. It sits at the intersection of applied AI, software engineering, backend development, and cloud architecture.
Your mission:
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Build AI-powered applications using Generative AI, Machine Learning, and LLM-based technologies.
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Design and implement RAG solutions, prompt-based systems, AI agents, embeddings, and semantic search.
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Integrate LLM APIs, open-source models, and AI components into enterprise applications.
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Develop backend services, REST APIs, microservices, and model-serving endpoints using Python and/or .NET C#.
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Work with structured and unstructured data across SQL, NoSQL, object storage, data lakes, and vector databases.
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Build data pipelines for ingestion, preprocessing, retrieval, and AI interaction.
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Evaluate AI systems for quality, relevance, hallucination, latency, cost, reliability, and security.
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Apply MLOps and LLMOps practices, including CI/CD, monitoring, evaluation, versioning, and feedback loops.
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Deploy AI services to cloud environments, especially Azure, using containers and orchestration platforms.
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Collaborate with product, engineering, data, architecture, security, and business teams.
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Ensure solutions follow responsible AI, privacy, security, scalability, and maintainability standards.
