Databricks AI Architect

Experteer Overview In this role you will design and lead the delivery of intelligent data platforms and AI-native solutions, enabling advanced analytics and autonomous decision-making at scale. You will translate business strategy into governed AI-ready architectures within Capgemini’s Data Platforms team, leveraging Databricks Lakehouse and Azure AI. You’ll drive delivery from data foundations to AI outcomes, collaborating across engineering, data, and business functions. This is a chance to shape enterprise AI adoption with governance, security, and scalable patterns in a hybrid-working environment. Pay / Benefits * Design scalable, secure Databricks Lakehouse architectures unifying data, analytics, and AI. * Leverage GenAI and developer tooling to accelerate delivery with production-ready pipelines and infrastructure. * Build enterprise-grade data pipelines and AI workflows using reusable, governed patterns. * Embed GenAI capabilities (including RAG, vector search, AI assistants) into data platforms. * Design and orchestrate AI agents and multi-agent workflows for intelligent automation. * Apply MLOps and LLMOps to ensure scalability and maintainability of AI solutions. * Monitor, optimise platform performance, cost, and model effectiveness. * Collaborate with engineering, data, and business teams to deliver end-to-end solutions. * Stay ahead of Databricks AI and Azure capabilities and apply to real-world use cases. Tasks * Proven experience designing and delivering data and AI platforms using Databricks. * Strong knowledge of Databricks Lakehouse, Unity Catalog, MLflow, and AI workloads. * Experience developing GenAI solutions such as RAG, embeddings, and LLM integration. * Experience designing agentic workflows and orchestration patterns. * Strong understanding of Azure AI services and their integration with Databricks. * Experience using VS Code and AI-assisted development tools such as GitHub Copilot. * Proven ability to accelerate SDLC using automation, templates, and standardised engineering approaches. * Experience implementing CI/CD pipelines and infrastructure as code. * Strong communication, stakeholder engagement, and leadership skills. Key requirements * hybrid working * flexible working arrangements * learning for life mindset * 250,000 courses and external certifications * hackathons and thinktanks

and data platforms. * Design and orchestrate AI agents and multi-agent workflows for intelligent automation. * Apply MLOps and LLMOps to ensure scalability and maintainability of AI solutions. * Monitor, optimise platform performance, cost, and model effectiveness. * Collaborate with engineering, data, and business teams to deliver end-to-end solutions. * Stay ahead of Databricks AI and Azure capabilities and apply to real-world use cases. Tasks * Proven experience designing and delivering data and AI platforms using Databricks. * Strong knowledge of Databricks Lakehouse, Unity Catalog, MLflow, and AI workloads. * Experience developing GenAI solutions such as RAG, embeddings, and LLM integration. * Experience designing agentic workflows and orchestration patterns. * Strong understanding of Azure AI services and their integration with Databricks. * Experience using VS Code and AI-assisted development tools such as GitHub Copilot. * Proven ability to accelerate SDLC using automation, aaaaa using and standardised engineering approaches. * Experience implementing CI/CD pipelines and infrastructure as code. * Strong communication, stakeholder engagement, and leadership skills. Key requirements * hybrid working * flexible working arrangements * learning for life mindset * 250,000 courses and external certifications * hackathons and thinktanks