Job purpose
Lead the design and implementation of the Risk AI Platform (Risk OS) by establishing a scalable data, semantic, and integration architecture that connects multiple AI-driven business applications through a common data layer, governance framework, metadata strategy, and shared services model. The role will define the target-state architecture for AI applications, Snowflake-based data assets, APIs, and enterprise integrations while ensuring solutions are production-ready, audit-ready, compliant, and aligned to enterprise technology standards.
The architect will serve as a hands-on technical leader, bridging business teams building AI applications with IT, infrastructure, security, and data teams to accelerate industrialization and deployment of AI solutions.
Key responsibilities
- Define and evolve the enterprise architecture for the Risk AI Platform, including data models, semantic models,
metadata standards, integration patterns, API strategy, and shared data services across multiple AI
applications.
- Design and implement a unified data architecture leveraging Snowflake as the central data layer, enabling
reuse of common datasets, APIs, business entities, risk opinions, assessments, and historical records across
applications.
- Establish metadata, governance, lineage, and semantic standards using enterprise data governance practices
and tools such as Collibra to improve interoperability, discoverability, and consistency of data assets.
- Partner with business users, AI application teams, infrastructure, and IT teams to productionize AI-generated
applications, including architecture reviews, deployment standards, code reviews, GitHub integration, UAT
support, and operational readiness.
- Define integration standards for Snowflake, SharePoint, Bloomberg APIs, Azure services, web applications, AI
agents, and future enterprise platforms while promoting reusable services and common architectural patterns.
- Provide technical leadership and architectural guidance for AI, GenAI, agent-based solutions, MCP-enabled
architectures, and enterprise AI governance, ensuring scalability, security, compliance, and audit requirements
are embedded into all solutions
- Mentor architects and delivery teams with strong technical leadership
- Own outcomes from vision to implementation, balancing business, technology, and risk
Key competencies
Required Qualifications
- 16+ years in enterprise technology consulting
- Architecture & Data: Enterprise Architecture, Data Architecture, Information Architecture, Semantic Modeling,
Metadata Management, Data Governance, Canonical Data Modeling, Snowflake Architecture, API Design,
Integration Architecture, and Enterprise Platform Design.
- AI & Technology: Generative AI Architecture, Agentic AI, MCP Frameworks, AI Application Productionization,
Azure Cloud Services, GitHub, DevOps Practices, SharePoint Integration, API Management, Knowledge
Graphs, and Enterprise AI Governance.
- Leadership & Consulting: Strategic thinking, stakeholder management, architecture governance, advisory
consulting, cross-functional collaboration, problem-solving, decision-making, communication with business and
IT leadership, and the ability to define target-state architectures and implementation roadmaps in greenfield
environments.
