Capgemini invites skilled professionals to contribute to shaping the future of technology and build a more sustainable, inclusive world. As a Python & GenAI Engineer, you will be instrumental in empowering leading organizations to unlock the full potential of cutting-edge technology. We offer a collaborative environment where your career growth and professional development are paramount, supported by opportunities for certifications and training in emerging technologies.

Join our global team of 420,000 professionals across more than 50 countries. With a rich heritage of nearly 60 years, Capgemini is a responsible and diverse group committed to delivering tangible business value. We leverage our deep industry expertise and a robust partner ecosystem to provide end-to-end services and solutions, encompassing strategy, technology, design, engineering, and business operations. In 2024, the Group reported global revenues of €22.1 billion. Make it real with us at www.capgemini.com.

Develop and enhance cutting-edge Generative AI applications utilizing Large Language Models (LLMs) and advanced AI frameworks.

Build and optimize Retrieval Augmented Generation (RAG) pipelines, vector search capabilities, and AI-driven workflows. Design effective prompts and fine-tune models using techniques such as LoRA and QLoRA.

Develop robust REST APIs and seamlessly integrate AI functionalities into enterprise applications. Deploy, monitor, and maintain AI solutions within cloud and containerized environments, ensuring high availability and performance.

Champion code quality through rigorous testing, effective debugging, comprehensive documentation, and thorough code reviews. Adhere to established Responsible AI principles, security protocols, and data governance best practices throughout the development lifecycle.

Demonstrate strong proficiency in Python, Object-Oriented Programming (OOP), API development, debugging, and adherence to software development best practices.

Possess a solid understanding of Data Structures & Algorithms, complexity analysis, and advanced problem-solving techniques. Showcase hands-on experience with LLMs, Prompt Engineering, RAG, AI Agents, and embeddings.

Exhibit familiarity with key frameworks such as LangChain, LangGraph, LlamaIndex, and Hugging Face. Knowledge of vector databases, semantic/hybrid search, and retrieval architectures is essential.

Experience with deep learning libraries like PyTorch, TensorFlow, or Keras is required. Familiarity with Docker, Git, CI/CD pipelines, and major cloud platforms (Azure/AWS/GCP) is expected. A foundational understanding of AI governance, data privacy, and Responsible AI principles is crucial.