Agentic AI Architect

Capgemini•Atlanta, GA

1d•Hybrid

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. We are seeking a highly experienced Agentic AI Architect to lead the design and implementation of enterprise-scale Agentic AI solutions. This role will be responsible for defining architecture, guiding development teams, and ensuring secure, scalable, and high-performing AI systems that leverage Large Language Models (LLMs), multi-agent frameworks, and enterprise integrations. The ideal candidate combines deep expertise in AI/ML, software architecture, and enterprise solution design, with a proven track record of delivering complex, production-grade AI systems.

Requirements

Deep expertise in AI/ML, software architecture, and enterprise solution design.
Proven track record of delivering complex, production-grade AI systems.
Experience with agent frameworks like LangChain, Semantic Kernel, AutoGen, CrewAI.
Experience with LLM platforms such as Azure OpenAI, OpenAI, Anthropic.
Experience with Retrieval-Augmented Generation (RAG) pipelines.
Experience with knowledge retrieval architectures, memory management systems, and tool integration frameworks.
Experience with AI governance, Responsible AI principles, security, privacy, and data governance.
Experience with enterprise integration strategies and patterns.
Strong technical leadership and mentorship skills.
Excellent communication and stakeholder collaboration skills.

Responsibilities

Define end-to-end architecture for enterprise Agentic AI solutions.
Design multi-agent systems, orchestration layers, and enterprise integrations.
Establish architectural standards, design patterns, and best practices.
Design and implement scalable agent frameworks using technologies such as: LangChain, Semantic Kernel, AutoGen, CrewAI, and other emerging agent orchestration frameworks.
Build robust and maintainable AI architectures capable of enterprise-scale deployment.
Lead the development of LLM-driven solutions using: Azure OpenAI, OpenAI, Anthropic, and similar foundation model platforms.
Develop effective prompting strategies and optimize model interactions.
Design solutions that maximize accuracy, performance, and reliability.
Architect and implement Retrieval-Augmented Generation (RAG) pipelines, knowledge retrieval architectures, memory management systems, and tool integration frameworks.
Enable AI agents to effectively access and utilize enterprise knowledge.
Establish AI governance frameworks and standards.
Implement Responsible AI principles and controls.
Ensure compliance with security, privacy, and data governance requirements.
Define risk management practices for enterprise AI solutions.
Design integration strategies with enterprise applications and platforms, including: CRM systems, ERP platforms, APIs, Data platforms, and Third-party services.
Develop scalable and secure enterprise integration patterns.
Provide technical leadership and mentorship to engineering teams.
Guide architecture decisions and implementation approaches.
Review code quality, scalability, and overall solution design.
Drive engineering excellence and innovation across teams.
Partner with business and technology stakeholders to translate business requirements into technical solutions.
Communicate architectural strategies and implementation roadmaps to executive leadership.
Ensure AI systems meet targets for: Performance, Scalability, Reliability, Cost optimization.
Continuously evaluate emerging AI technologies, tools, and frameworks to drive innovation.