Senior AI Architect – Finance AI Enablement

ZipStaff•San Antonio, TX

2d•Remote

About The Position

ZipStaff is recruiting on behalf of a leading financial services organization for a Senior AI Architect serving as the top AI/ML technical authority for the finance IT organization. This highly senior, hands-on architecture role translates business transformation goals into AI/ML technical strategy, designs end-to-end architectures, and guides engineering teams through detailed design and implementation. The architect shapes multi-year roadmaps with business and technology leaders, produces reference architectures and design standards, and works directly with engineers on complex design problems. The role is remote and requires U.S. citizenship or a Green Card. Employment is a W2 contract with the client; C2C and 1099 arrangements are not accepted. The architect establishes the strategy, platforms, and practices for GenAI, machine learning, automation, and agentic systems across finance technology. Solutions support financial use cases including fraud detection, forecasting, reconciliation, reporting, analytics, and intelligent document, content, and audio processing. The role owns architectural quality, scalability, and performance for high-volume financial data processing and analytics, while ensuring enterprise, regulatory, and financial-data governance requirements are met. It also drives MLOps/LLMOps adoption for model lifecycle management, monitoring, and continuous improvement.

Requirements

12+ years of progressive experience in enterprise architecture, cloud architecture, and distributed systems/platform solutions.
Hands-on experience across Azure, GCP, and AWS.
Deep experience with Kubernetes and container-based platform architecture.
Strong background in event-driven architectures/frameworks and high-volume, high-throughput data processing systems.
Demonstrated experience architecting production GenAI and machine learning solutions.
Experience in financial data processing and analytics, ideally in a finance, banking, or FinTech IT organization.
Track record architecting AI/ML foundational frameworks or platforms used across multiple teams or use cases.
Strong technical depth in GenAI, including LLM application architecture, RAG, Knowledge Graphs, GraphRAG, and vector databases; agentic AI; machine learning for forecasting, fraud detection, and anomaly detection; automation; and analytics AI.
Ability to work effectively at both strategic executive/business-stakeholder and detailed engineering levels.
Strong stakeholder management, communication, mentoring, and technical leadership skills.
Ability to work fully remotely.
Must be a U.S. citizen or Green Card holder; the posting also describes this status as preferred.

Nice To Haves

Prior experience in Finance Technology, banking, or a regulated financial services environment.
Experience with MLOps/LLMOps tooling and model governance/risk frameworks.
Relevant cloud or architecture certifications, such as Azure Solutions Architect, GCP Professional Cloud Architect, AWS Solutions Architect, or TOGAF.
Hands-on experience designing semantic layers and agentic workflows for complex financial data models.

Responsibilities

Define the AI/ML technical strategy supporting the finance organization’s broader business transformation vision.
Partner with business leaders and senior stakeholders to turn financial and operational goals into AI/ML and automation initiatives.
Establish and promote an AI-first architectural philosophy across finance technology.
Advise business and technology executives on AI/ML capabilities, feasibility, risk, and value.
Create high-level AI/ML and automation architecture designs and multi-year technology roadmaps aligned with business priorities.
Architect reusable AI/ML foundational frameworks and platforms for finance use cases, including fraud detection, forecasting, reconciliation, reporting, analytics, and intelligent document, content, and audio processing.
Design complex semantic layers using techniques such as RAG, Knowledge Graphs, and GraphRAG.
Define reference architectures for agentic AI workflows, including multi-agent orchestration, tool use, memory, and human-in-the-loop patterns.
Set AI/ML solution delivery architecture and design standards, frameworks, and best practices.
Produce detailed architecture designs for engineering teams to implement.
Guide and mentor engineers on technical design decisions, reviews, and solution implementation.
Own architectural quality, scalability, and performance for high-volume financial data processing and analytics pipelines.
Ensure solutions meet enterprise, regulatory, and financial-data governance requirements.
Drive adoption of MLOps/LLMOps practices for model lifecycle management, monitoring, and continuous improvement.