Data and AI Solution Architect

About Kearney Activate

Kearney Activate is the business acceleration and enablement arm of Kearney Management Consulting, helping clients accelerate transformation through Data and AI.

Our Data & AI business line bridges strategy, technology, and execution to deliver data-driven, AI-enabled, and cloud-powered transformations. We partner with leading platforms such as Microsoft, AWS, Snowflake, Databricks and Nvidia to help organizations build trusted data foundations, deploy intelligent solutions, and scale innovation.

We operate across four key focus areas:

• Advisory & Assurance Services: Driving strategy, governance, and change management to ensure impactful transformation.

  
  

• Data Services: Building trusted, high-quality data foundations using Agentic AI across engineering, architecture, and governance.

  
  

• Insights Services: Unlocking value through analytics, data science, and AI-driven storytelling for better decision making.

  
  

• Asset Services: Developing reusable GenAI assets, accelerators, and AI-powered solutions that push the boundaries of innovation.

  
  

Position Overview

We are seeking a visionary AI-Enabled Solution Architect to help lead the regional design and delivery of next-generation, AI-native data and analytics solutions. This role is central to our transformation team as we embed Generative AI, Agentic Intelligence, and modern data engineering into the core of how we operate and deliver value to our clients.

You’ll not only shape cutting-edge architectures but also lead and inspire multidisciplinary teams, helping to develop the next generation of technical leaders who carry our depth of expertise into complex client environments. You'll operate at the intersection of technology and business, translating emerging capabilities into practical, high-impact solutions.

Your Responsibilities

• Architect scalable, modular platforms that integrate modern data engineering with GenAI and Agentic AI capabilities.

  
  

• Design and build end-to-end ingestion, transformation, and classification pipelines across structured and semi structured data sources (e.g., Excel, JSON).

  
  

• Implement RAG (Retrieval-Augmented Generation) pipelines using intelligent chunking strategies and multi