Lead Data & AI Architect - GCP

Job Title:

Lead Data AI Architect(GCP)

Location:

Charlotte, NC (Min 3 days onsite) JD:
Lead Data & AI Architect GCP

Location:

Charlotte, United States Work Model:
Client-facing; minimum 3 days per week onsite

Role Overview We are seeking a highly experienced Lead Data & AI Architect & Strategy consultant with deep expertise in Google Cloud Platform (GCP) to lead the architecture for a strategic Data and AI transformation program within a leading financial-services organization. This is a critical technology leadership role with end-to-end responsibility for defining and governing the Data and AI architecture across the transformation lifecycle, from current-state discovery and assessment through target-state architecture, implementation planning and implementation.

The role will establish the architectural direction, drive key technology decisions and provide technical leadership to ensure the platform is scalable, secure, resilient and capable of supporting enterprise-grade Data, AI and agentic AI use cases. The role will work closely with senior stakeholders across Data, AI, Technology, Architecture, Security, Risk and Business functions, while providing technical direction to architects, engineers and delivery teams.

The successful candidate will combine deep GCP expertise with strong enterprise Data and AI architecture, consulting and leadership experience, and will be expected to translate business objectives into pragmatic technology solutions and measurable outcomes.

Key Responsibilities Architecture & Technology Leadership Own the end-to-end Data and AI architecture for the transformation program and establish the target-state technology vision. Lead current-state architecture assessments, identify technical gaps and dependencies, and define the target-state architecture and modernization strategy. Translate business and technology requirements into scalable, secure and implementation-ready architecture. Define architecture principles, reference architectures, technology standards and reusable patterns for enterprise adoption.

Drive architecture decisions across data, AI, cloud, integration, security, governance and observability. Lead architecture governance, design reviews and technical decision-making throughout the program. Data & GCP Architecture Design and govern enterprise-scale data platforms on GCP supporting batch, real-time and streaming workloads. Architect data ingestion, processing, storage, transformation, serving and consumption patterns using GCP-native services. Provide deep architectural guidance across Big Query, Cloud Storage, Dataplex, Dataflow/Apache Beam, Pub/Sub, Dataproc, Vertex AI and related GCP services.

Define modern data architecture patterns, including lakehouse/Medallion architecture, enterprise data models and data products where appropriate. Design high-volume data workloads with appropriate approaches to performance, scalability, resilience, availability and cost optimization. Define enterprise integration patterns across applications, APIs, databases and other data sources. Establish Big Query architecture and optimization strategies, including data modeling, partitioning, clustering and workload management. AI, GenAI & Agentic AI Architecture Define the architecture required to support enterprise AI/ML, Generative AI and agentic AI capabilities.

Design the data, knowledge and platform foundations required for AI agents to securely access, retrieve and reason over enterprise information. Define architecture patterns for RAG, vector search, embeddings, knowledge retrieval and enterprise search. Architect integration between AI agents, enterprise data, APIs, tools and business applications to enable AI-driven business processes and outcomes. Define patterns for AI orchestration, multi-agent interaction, tool execution, human-in-the-loop workflows and agent observability where applicable.

Establish architecture for model development, deployment, monitoring and lifecycle management using capabilities such as Vertex AI. Ensure AI solutions incorporate appropriate guardrails, security, governance, responsible AI and model-risk controls. Security, Governance & Regulatory Architecture Ensure architecture aligns with financial-services security, regulatory, privacy and risk requirements. Define secure architecture patterns covering IAM, RBAC, service accounts, encryption, network security, data access, environment isolation and segregation of duties.

Incorporate data governance, classification, lineage, metadata, data quality, retention and auditability into the platform architecture. Partner with Security, Risk, Compliance and Governance teams to ensure architecture and implementation meet enterprise controls and standards. Establish appropriate governance and operational controls for AI/ML and agentic AI capabilities. Implementation & Engineering Leadership Translate the target-state architecture into a pragmatic implementation roadmap, including priorities, dependencies,…