Capgemini is seeking an experienced GenAI Engineer / Data Scientist with 5-7+ years of experience in AI, machine learning, and application development to design, develop, deploy, and maintain Generative AI and Predictive AI solutions that create business value and improve operational efficiency. Key responsibilities include designing and developing AI-powered applications and APIs; building and deploying Generative AI models (GPT, Claude, Stable Diffusion, RAG) and Predictive AI models (regression, classification, clustering); developing intelligent AI agents and agentic workflows for task automation; deploying, monitoring, and optimizing AI models in production; designing and maintaining data pipelines, data lakes, and data warehouses; collaborating with business and technical teams to align AI solutions with organizational goals; staying updated on emerging AI technologies and contributing to reusable assets, demos, and sales initiatives; and communicating AI concepts and outcomes to both technical and non-technical stakeholders. Required skills and experience include programming (Python, SQL); APIs (REST/JSON, HTTP requests, authentication, tokens, error handling, debugging, integrations, documentation); Cloud platforms (Azure, AWS, GCP); Big Data tools (Databricks, PySpark); AI/ML frameworks (LangChain, TensorFlow, PyTorch, Scikit-learn); MLOps & deployment (Docker, Azure DevOps, AWS ECS/EKS/Fargate, CI/CD pipelines); Databases (MySQL, MongoDB, Redis); knowledge of RAG architectures, AI agents, and multimodal AI systems; Graph databases such as Neo4j or Ontotext are a p

Capgemini is seeking an experienced GenAI Engineer / Data Scientist with 5-7+ years of experience in AI, machine learning, and application development to design, develop, deploy, and maintain Generative AI and Predictive AI solutions that create business value and improve operational efficiency. Key responsibilities include designing and developing AI-powered applications and APIs; building and deploying Generative AI models (GPT, Claude, Stable Diffusion, RAG) and Predictive AI models (regression, classification, clustering); developing intelligent AI agents and agentic workflows for task automation; deploying, monitoring, and optimizing AI models in production; designing and maintaining data pipelines, data lakes, and data warehouses; collaborating with business and technical teams to align AI solutions with organizational goals; staying updated on emerging AI technologies and contributing to reusable assets, demos, and sales initiatives; and communicating AI concepts and outcomes to both technical and non-technical stakeholders. Required skills and experience include programming (Python, SQL); APIs (REST/JSON, HTTP requests, authentication, tokens, error handling, debugging, integrations, documentation); Cloud platforms (Azure, AWS, GCP); Big Data tools (Databricks, PySpark); AI/ML frameworks (LangChain, TensorFlow, PyTorch, Scikit-learn); MLOps & deployment (Docker, Azure DevOps, AWS ECS/EKS/Fargate, CI/CD pipelines); Databases (MySQL, MongoDB, Redis); knowledge of RAG architectures, AI agents, and multimodal AI systems; Graph databases such as Neo4j or Ontotext are a p