23 days ago
Responsibilities
- Design and implement enterprise applications powered by Generative AI and Large Language Models.
- Build enterprise copilots, virtual assistants, intelligent agents, semantic search, and Retrieval-Augmented Generation solutions.
- Integrate foundation models, cognitive services, APIs, data platforms, and enterprise applications.
- Design data ingestion, transformation, enrichment, knowledge indexing, and vectorization pipelines.
- Manage structured and unstructured knowledge repositories and ensure data quality, consistency, and traceability.
- Deploy and operate AI solutions using Azure managed services and implement LLMOps, MLOps, and DevSecOps practices.
- Monitor solution performance, cost, accuracy, adoption, security, and production operations.
- Manage prompt, model, and AI component versioning and apply Responsible AI, data protection, compliance, and governance controls.
- Participate in architecture and technical quality reviews.
Requirements
- 5+ years of professional experience, including 2+ years implementing enterprise Generative AI solutions.
- Hands-on experience developing enterprise Generative AI solutions with Azure OpenAI Service and Microsoft Azure AI services.
- Experience designing and implementing Retrieval-Augmented Generation solutions and building AI agents, copilots, and intelligent virtual assistants.
- Strong programming skills in Python and/or C# (.NET).
- Experience with Semantic Kernel, LangChain, LangGraph, CrewAI, or similar AI orchestration frameworks.
- Experience integrating solutions through REST APIs and enterprise systems.
- Knowledge of Azure AI Search, embeddings, vector search technologies, SQL, and NoSQL databases.
- Experience managing structured and unstructured data for AI applications and implementing LLMOps, MLOps, and DevOps practices.
- Familiarity with Azure DevOps, GitHub Actions, CI/CD pipelines, Terraform, Bicep, or infrastructure as code.
- Experience deploying, operating, and supporting AI solutions in Azure production environments.
- Understanding of observability, monitoring, security, Responsible AI, and AI governance.
- Preferred experience includes Azure Machine Learning, Databricks, Microsoft Fabric, model fine-tuning, event-driven architectures, multi-agent systems, Docker, Kubernetes/AKS, or Azure Container Apps.
- Preferred certifications include Azure AI Engineer Associate AI-102, Azure Developer Associate AZ-204, Azure Solutions Architect Expert AZ-305, Microsoft Applied Skills – Azure OpenAI, DP-100 Azure Data Scientist Associate, and Databricks Generative AI Fundamentals.
Benefits
- Meaningful projects focused on real-world challenges, sustainability, inclusion, and digital equity.
- Continuous learning through internal academies, certifications, mentorship, digital learning platforms, and digital campuses focused on AI, Cloud, Data, and Sustainability.
- Career mobility across roles, industries, and geographies, including career acceleration programs.
- Remote, hybrid, or on-site work options depending on project needs and personal preferences, with offices available in CDMX, GDL, and AGS.
Categories
About Capgemini
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com