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# GenAI Architect

- **Company:** Capgemini
- **Location:** Denver, CO, United States
- **Experience:** Experienced
- **Salary:** $155,000.0 - $190,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Cloud Storage, Databases, High-Level Architecture, Python (Programming Language), Machine Learning, Open Source Technology, OpenAI, Cloud Services, Azure Machine Learning, Search Technologies, Software Engineering, Systems Integration, Openapi, Google Cloud, Enterprise Software Applications, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Agentic-AI, Containerization, AI Platforms, Kubernetes, Machine Learning Operations, Claude, GPT, Serverless Computing, Databricks, Agent2Agent Protocol
- **Published:** October 9, 2026
- **Apply:** https://dejobs.org/x/x/D815BF3732064C7988C9BBDC1B20B889/job/

## About the Role

* 8+ years of experience in software engineering, solution architecture, or technical leadership, including 3+ years of hands-on experience designing, developing, and implementing enterprise Generative AI solutions.
 * 2-3+ years of experience architecting and implementing multi-agent AI solutions , including designing autonomous agent workflows, orchestration frameworks, and enterprise-scale Agentic AI architectures.
 * 2-3+ years of experience designing Agent-to-Agent (A2A) communication architectures , enabling intelligent collaboration, coordination, and orchestration between autonomous AI agents within enterprise environments.
 * Strong hands-on expertise with Python , Large Language Models (LLMs) , Retrieval-Augmented Generation (RAG) , AI Agents, Agentic AI, prompt engineering, and modern AI application architectures.
 * Experience developing AI-powered applications using leading foundation models such as OpenAI GPT , Claude , and other commercial or open-source LLMs.
 * Experience designing and deploying scalable AI solutions across one or more major cloud platforms, including Microsoft Azure , Amazon Web Services (AWS) , and/or Google Cloud Platform (GCP) .
 * Experience leveraging cloud-native AI services such as Azure AI Services , Azure OpenAI , AWS Bedrock , Amazon SageMaker , Vertex AI , Databricks, Azure Functions, Azure Blob Storage, or similar technologies.
 * Strong understanding of Machine Learning concepts, model deployment, vector databases, semantic search, and enterprise AI application architecture.

## Description

Sogeti is seeking an experienced Principal GenAI Architect to lead the strategy, architecture, design, and implementation of enterprise-scale Generative AI solutions for our clients. The ideal candidate brings deep expertise in Generative AI, Agentic AI, multi-agent architectures, and Agent-to-Agent (A2A) communication , along with strong experience designing cloud-native AI solutions utilizing modern Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Machine Learning, and enterprise AI platforms across Azure, AWS, and GCP., * Lead the architecture, design, and implementation of enterprise-scale Generative AI, Agentic AI, multi-agent, and Agent-to-Agent (A2A) solutions that address complex business challenges.

 * Design scalable AI application architectures utilizing Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, vector databases, semantic search, and modern AI orchestration frameworks.
 * Architect and implement autonomous multi-agent workflows, intelligent agent orchestration, and Agent-to-Agent communication patterns across enterprise environments.
 * Partner with clients and business stakeholders to translate strategic business objectives into scalable, secure, and enterprise-ready AI solutions.
 * Design and implement AI solutions utilizing cloud-native technologies across Microsoft Azure, AWS, and GCP , including services such as Azure OpenAI, Azure AI Services, AWS Bedrock, Amazon SageMaker, Vertex AI, Databricks , and related AI platforms.
 * Create and maintain High-Level Designs (HLDs), Low-Level Designs (LLDs), architecture diagrams, API specifications, technical documentation, and implementation roadmaps.
 * Design and oversee integrations between AI platforms and enterprise applications, cloud services, APIs, databases, and third-party systems.