GCP AI Architect for Enterprise GenAI & LLM Solutions

Capgemini; Chicago (IL); On-site; USD 180,000 - 240,000; Full time

Job summary

Capgemini in Chicago seeks an experienced GCP AI Architect to lead design and implementation of enterprise AI, Generative AI, ML, and data analytics solutions on Google Cloud Platform. You will shape architecture, governance, and transformation programs across the business.

You will design scalable, secure cloud-native architectures using Vertex AI, BigQuery and Dataflow, establish MLOps and LLMOps, and mentor teams through multi-region deployments and DR strategies.

Qualifications

• 8+ years of experience in cloud architecture, software engineering, or data platforms.
• 3+ years designing AI/ML solutions on GCP.
• Experience leading enterprise-scale cloud transformation programs.
• Experience architecting Generative AI and LLM-based applications.

Responsibilities

• Design end-to-end AI, Generative AI, and ML solution architectures on GCP.
• Define enterprise AI strategies, roadmaps, and reference architectures.
• Architect Retrieval-Augmented Generation (RAG), Agentic AI, and AI-powered business applications.
• Evaluate and select appropriate Foundation Models, Gemini models, and open-source LLMs.
• Design scalable, secure, and highly available cloud-native architectures using Vertex AI, BigQuery, Cloud Storage, Dataflow etc
• Define multi-region and disaster recovery strategies.
• Establish enterprise MLOps and LLMOps frameworks.
• Create data lakes, lakehouses, and enterprise analytics solutions.

Skills

GCP Architecture

AI/ML Solutions

Cloud Transformation

Enterprise AI

Tools

Vertex AI

BigQuery

Dataflow

Job description

Capgemini in Chicago seeks an experienced GCP AI Architect to lead design and implementation of enterprise AI, Generative AI, ML, and data analytics solutions on Google Cloud Platform. You will shape architecture, governance, and transformation programs across the business.

You will design scalable, secure cloud-native architectures using Vertex AI, BigQuery and Dataflow, establish MLOps and LLMOps, and mentor teams through multi-region deployments and DR strategies.