Senior Azure AI Architect

At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client's challenges of today and tomorrow. Informed and validated by science and data. Superpowered by creativity and design. All underpinned by technology created with purpose. Your Role • Lead architecture and technical design for enterprise Azure AI solutions across GenAI, agentic AI, RAG, AI Search, integration, security, observability and AI Factory platform patterns. • Design Azure AI architectures for GenAI, agentic AI, RAG, enterprise copilots and AI Factory platforms. • Define patterns across Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML, Fabric, Databricks and integration services. • Lead technical discovery with IT, data, security and enterprise architecture stakeholders. • Create reference architectures, proposal solution blueprints, technical assumptions and PoC plans. • Guide MVPs covering environment setup, secure connectivity, data ingestion, retrieval, agents, evaluation and monitoring. • Embed guardrails, content safety, logging, identity, access control and human oversight into designs. Experience And Skills Required • 8-12+ years in solution architecture, cloud architecture, data/AI architecture or platform engineering. • 4-5+ years designing Azure solutions, ideally with production AI or client-facing implementations. • Hands-on GenAI, RAG, LLM app, agentic AI, MLOps/LLMOps or AI platform engineering exposure. • Secure enterprise integration experience: identity, APIs, private networking, monitoring and cloud governance.Strong experience in Python is a plus but it’s knowledge is mandatory. • Azure AI Foundry and Azure OpenAI. • Azure AI Search, vector search, embeddings and RAG. • Azure Machine Learning, evaluation and deployment concepts. • Microsoft Fabric, Azure Databricks, ADLS Gen2 and data integration. • Azure Functions, Logic Apps, Event Grid, Service Bus and API Management. • Entra ID, managed identities, Key Vault, private endpoints, VNet integration, RBAC and logging. • Agentic AI, tool calling, orchestration, memory, observability and evaluation. • Preferred certifications: Azure Solutions Architect Expert and Azure AI Engineer Associate. • Experience in AI and Cloud FinOps 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. Show more Show less

8-12+ years in solution architecture, cloud architecture, data/AI architecture or platform engineering; 4-5+ years designing Azure solutions, ideally with production AI or client-facing implementations; Hands-on GenAI, RAG, LLM app, agentic AI, MLOps/LLMOps or AI platform engineering exposure; Secure enterprise integration experience: identity, APIs, private networking, monitoring and cloud governance. Strong experience in Python is a plus but it’s knowledge is mandatory; Preferred certifications: Azure Solutions Architect Expert and Azure AI Engineer Associate; Experience in AI and Cloud FinOps.

Lead architecture and technical design for enterprise Azure AI solutions across GenAI, agentic AI, RAG, AI Search, integration, security, observability and AI Factory platform patterns; Design Azure AI architectures for GenAI, agentic AI, RAG, enterprise copilots and AI Factory platforms; Define patterns across Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML, Fabric, Databricks and integration services; Lead technical discovery with IT, data, security and enterprise architecture stakeholders; Create reference architectures, proposal solution blueprints, technical assumptions and PoC plans; Guide MVPs covering environment setup, secure connectivity, data ingestion, retrieval, agents, evaluation and monitoring; Embed guardrails, content safety, logging, identity, access control and human oversight into designs.