AI Solutions Architect

Description

The Kinsley Group is seeking an AI Solutions Architect to lead the design, implementation, and governance of the organization’s enterprise data, analytics, and AI ecosystem. This role is responsible for centralizing data, semantic models, analytics, and governance under IT, while enabling scalable, trusted, and AI-ready holistic solutions across the organization.

The AI Solutions Architect owns the enterprise data and analytics architecture, establishes data governance and semantic standards, and develops an AI Strategy Roadmap with an executable delivery plan aligned to business priorities. This role serves as a strategic bridge between IT, business leadership, and operational teams—ensuring data is governed, reusable, secure, and positioned to support advanced analytics and future AI initiatives.

Core platforms may include Microsoft Fabric, Power BI, and Microsoft Purview.

AI Strategy, Architecture & Enablement

The architect aligns AI strategy with business objectives, manages delivery and drives automation and analytics improvements responsibly.

• Align AI strategy with the organization’s business goals and current data maturity.
• Maintain clear, actionable AI delivery plans that prioritize operational efficiency and organizational value.
• Establish production-ready, governed AI architecture for scalable deployment.
• Direct use cases focused on automation and analytics improvements to drive business value.

Leadership, Collaboration & Execution

The AI Solutions Architect leads data, analytics, and AI initiatives, aligning solutions with business goals and guiding teams.

• Serve as the technical and strategic lead for AI initiatives and data analytics.
• Partner with executives, IT, and business leaders to ensure alignment with organizational objectives.
• Executes and takes ownership of technical implementation and strategic execution of AI initiatives.
• Ensure efficient, high-integrity delivery of initiatives that create measurable business value.
• Clearly explain technical concepts to audiences with varying levels of expertise.

Enterprise Data & Analytics Architecture

The AI Solutions Architect develops secure, scalable enterprise data architecture and manages the central Microsoft Fabric data platform.

• Define and manage enterprise data architecture to support organizational growth and protect sensitive information.
• Oversee the centralized data platform on Microsoft Fabric, ensuring seamless integration and accessibility.
• Standardize semantic models to provide consistent metrics and KPIs across the organization.
• Design integration patterns that accommodate a variety of data sources, supporting both legacy and modern systems.
• Identify and assess the integration or application of emerging technologies to explore potential business opportunities.
• Enable advanced analytics and AI/ML workloads by creating robust, scalable, and flexible data architecture.

Data Governance, Quality & Compliance

This role leads data governance, sets ownership and standards for quality and compliance, oversees Microsoft Purview, and drives data remediation.

• Lead data governance by defining ownership, standards, and policies for enterprise data assets.
• Manage Microsoft Purview to support data cataloging and governance activities.
• Enforce data quality standards and coordinate remediation processes when necessary.
• Promote governance through effective documentation and ongoing training initiatives.
• Oversee the data architecture to help deliver certified datasets and reliable reporting solutions.
• Balance self‑service analytics opportunities with centralized controls to maintain data integrity.
• Deliver reusable, enterprise‑grade analytics solutions for consistent business insights.

• Core Platforms:
  Microsoft Fabric, Power BI, Microsoft Purview, Azure Data Platform
• Preferred Programming & Query
  
  Languages:
  
  SQL, Python, PySpark / Spark SQL, DAX, KQL
• AI & Machine Learning:
  Familiarity with Python ML libraries, feature engineering, model lifecycle management, and AI governance
• AI & Machine Learning:
  Familiarity with Python ML libraries, feature engineering, model lifecycle management, AI governance,…