Agentic AI Architect

Capgemini•Atlanta, GA

About The Position

Capgemini is seeking a highly experienced Agentic AI Architect to lead the design and implementation of enterprise-scale agentic AI solutions. The role defines architecture, guides development teams, and ensures AI systems that use LLMs, multi-agent frameworks, and enterprise integrations are scalable, secure, high-performing, reliable, and cost-optimized. The position combines deep AI/ML expertise with architectural design experience and delivery of complex, production-grade solutions. The architect will establish end-to-end designs for agentic AI, including multi-agent systems, orchestration, RAG pipelines, memory systems, tool integrations, and connections to enterprise platforms. The role also provides technical leadership, mentorship, and code quality oversight; works with business stakeholders to translate requirements into technical solutions; and evaluates emerging AI technologies and frameworks. The posting lists a base compensation range of $125,000–$140,000 per year. Regular, full-time employees may be eligible for a benefits package, subject to local policy and eligibility.

Requirements

10+ years of experience in software engineering, AI/ML, or solution architecture.
Strong expertise in Python and modern backend architectures.
Hands-on experience with LLM ecosystems and agent frameworks.
Proven experience designing multi-agent systems and distributed architectures.
Deep understanding of prompt engineering and LLM optimization.
Deep understanding of embeddings and vector databases, such as FAISS, Pinecone, or Azure AI Search.
Experience with Retrieval-Augmented Generation (RAG).
Experience with cloud platforms; Azure preferred, with AWS or GCP acceptable.
Strong knowledge of API design, microservices, and event-driven architecture.
Familiarity with security, authentication such as OAuth and SSO, and data governance.
Experience leading technical teams and driving architecture decisions.

Nice To Haves

Experience with Model Context Protocol (MCP) or similar integration standards.
Exposure to enterprise AI transformation programs.
Knowledge of MLOps or LLMOps frameworks.
Experience with frontend or conversational UI frameworks.
Certifications such as Azure AI Engineer or Solutions Architect.

Responsibilities

Define end-to-end architecture for agentic AI solutions, including multi-agent systems, orchestration layers, and integrations.
Design and implement scalable agent frameworks using technologies such as LangChain, Semantic Kernel, AutoGen, and CrewAI.
Lead development of LLM-driven systems using platforms such as Azure OpenAI, OpenAI, and Anthropic, including robust prompting strategies.
Architect RAG pipelines, memory systems, and tool integration layers.
Establish best practices for AI governance, security, and Responsible AI.
Design integration strategies for enterprise systems such as CRM, ERP, APIs, and data platforms.
Provide technical leadership, mentorship, and code quality oversight to development teams.
Collaborate with business stakeholders to translate requirements into technical solutions.
Ensure AI systems’ performance, scalability, reliability, and cost optimization.
Evaluate emerging AI technologies and frameworks to drive innovation.

Benefits

Paid time off, including vacation (12–25 days depending on grade), company-paid holidays, personal days, and sick leave, according to policy.
Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
Retirement savings plans, such as a 401(k) in the U.S. or RRSP in Canada.
Life and disability insurance.
Employee assistance programs.
Other benefits as provided by local policy and eligibility.
The role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

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