Principal Application Architect (Enterprise AI), Digital Payments Platform

About the Position

We are looking for a Principal Application Architect who will define and drive the long term architectural vision for a large scale digital and AI enabled enterprise platform. You will operate at a strategic level, aligning business objectives with technology execution while influencing organisation wide architecture decisions, investment priorities, and innovation initiatives.

About the Project

You will work on a mission critical enterprise platform supporting payments, data, and AI driven capabilities across multiple regions. The platform focuses on scalability, resilience, and regulatory compliance while enabling continuous digital innovation.

About the Team

You will collaborate with a global architecture and engineering community including application architects, platform engineers, data specialists, and product leaders. The team works in a distributed model with close alignment across engineering, product, risk, and operations functions.

Responsibilities

• Define and own the enterprise wide application and AI architecture strategy for the platform.
• Establish architecture principles, standards, and operating models across multiple domains and teams.
• Lead architecture governance forums and drive alignment across programmes and portfolios.
• Shape multi year technology and AI roadmaps aligned with business growth and innovation goals.
• Advise executive stakeholders on technology investments, architectural trade offs, and risk considerations.
• Oversee the design of complex, business critical platforms across payments, data, and AI domains.
• Act as the escalation point for significant architectural decisions impacting the enterprise landscape.
• Define and lead enterprise AI architecture including data platforms, model lifecycle management, and intelligent automation.
• Ensure responsible, ethical, and compliant adoption of AI solutions at scale.
• Drive platform based architecture evolution with a strong focus on APIs, shared capabilities, and reuse.
• Embed security, resilience, and data protection into all architectural decisions.
• Communicate architectural vision and strategy clearly to senior and executive stakeholders.
• Build, mentor, and develop an architecture community while promoting engineering excellence and continuous improvement.

Requirements

• Extensive experience leading architecture for large scale enterprise platforms in cloud environments, preferably AWS.
• Deep expertise in distributed systems, microservices, event driven architecture, and platform engineering.
• Strong background in data and AI architecture, including large scale data platforms and machine learning or large language model systems.
• Proven experience defining and operating enterprise architecture standards and governance models.
• Demonstrated experience operating at principal level with responsibility for influencing organisation wide technology decisions.
• Proven ability to lead cross functional and globally distributed teams and to contribute to large transformation programmes.
• Strong executive presence with the ability to influence senior and C suite stakeholders.
• Significant experience in fintech, payments, or broader financial services environments.
• Strong understanding of regulatory, compliance, and risk considerations and their impact on architectural decisions.
• Exceptional ability to communicate complex architecture concepts to both technical and non technical audiences.
• Strong decision making skills with the ability to balance delivery speed, risk management, and long term value.

Nice to Have

• Experience leading enterprise wide AI transformation initiatives.
• Knowledge of enterprise architecture frameworks such as TOGAF.
• Experience with platform operating models and internal developer platforms.
• Background delivering large scale digital transformation or innovation initiatives.

Technologies

AWS, cloud native services, microservices architecture, event driven systems, API first platforms, large scale data platforms, machine learning systems, large language models, MLOps tooling, CI CD pipelines, security and compliance tooling.