Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
Your Role
As an AI / LLM Engineer at Capgemini, you will design, develop, and optimize advanced solutions based on Large Language Models (LLMs), focusing on retrieval-augmented generation (RAG) and intelligent search systems.
You will be responsible for implementing and enhancing chunking and embedding strategies for text and images, improving how data is indexed, retrieved, and consumed by AI systems.
This role requires strong experience in prompt engineering, hybrid search, re-ranking techniques, AI guardrails, and evaluation frameworks, ensuring high-quality, safe, and scalable AI solutions.
Responsibilities & Scope
- Design and implement chunking and embedding strategies for text and image data
- Develop and optimize prompt engineering techniques for LLM-based systems
- Implement hybrid search and re-ranking mechanisms to improve retrieval accuracy
- Enrich data chunks using metadata filtering and structuring techniques
- Build, test, and validate AI guardrails to ensure safety, quality, and compliance
- Perform AI client testing and evaluation to validate model outputs and performance
- Improve retrieval accuracy and response quality in RAG-based architectures
- Collaborate with AI/ML teams to optimize models and system performance
- Contribute to the design of scalable, high-performance AI retrieval systems
Your Profile
AI & LLM Expertise – Strong understanding of LLMs, NLP concepts, and RAG architectures, with the ability to design and optimize intelligent retrieval systems.
Prompt & Retrieval Optimization – Skilled in prompt engineering, embeddings, chunking strategies, and improving search relevance with hybrid techniques.
Data & Search Engineering Mindset – Ability to structure, enrich, and filter data using metadata strategies to enhance AI performance.
Quality & Safety Focus – Experience implementing AI guardrails, validation frameworks, and testing methodologies to ensure responsible AI usage.
Collaborative & Adaptive – Works effectively with cross-functional teams in dynamic environments, driving innovation and continuous improvement.
What you’ll love
Exposure to top global companies working with Capgemini (145 of the Fortune 500 companies)
Tech solutions and projects driving societal impact and paving the way for a sustainable future
Well-being hub and different wellbeing initiatives
Need to know
Career Development in Spanish & English: Training, certifications, and mentorship programs available in both languages to support bilingual growth.
Community Engagement: Participate in local volunteering initiatives, tech meetups, and cultural events that connect you with the Capgemini community.
About Capgemini
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.
At Capgemini Mexico, we aim to attract the best talent and are committed to creating a diverse and inclusive work environment, so there is no discrimination based on race, sex, sexual orientation, gender identity or expression, or any other characteristic of a person. All applications welcome and will be considered based on merit against the job and/or experience for the position.
