Enterprise Architect - Chief Architect for Agentic Delivery

The world of payment processing is rapidly evolving, and businesses are looking for loyal and strategic partners, to help them grow.

Meet Nuvei, Nuvei is the global fintech building the infrastructure for every payment, everywhere. Its modular, flexible, and scalable technology enables leading companies to accept next-generation payments, offer all payout options, and benefit from card issuing, banking, risk, and fraud management services. Connecting businesses to their customers in more than 200 markets, with local acquiring in 52 markets, 150 currencies, and over 720 alternative payment methods, Nuvei provides the technology and insights for customers and partners to succeed locally and globally through one integration.

At Nuvei, we live our core values, and we thrive on solving complex problems. We're dedicated to continually improving our product and providing relentless customer service. We are always looking for exceptional talent to join us on the journey!

The Enterprise Architect - Agentic AI and Delivery Lifecycle will define and lead the enterprise architecture for the company"s adoption of agentic AI across Product, Software Engineering, and Platform Engineering.

The role will establish the company-wide Agentic Delivery Lifecycle (ADLC) and govern the platforms, security controls, reusable capabilities, observability, and cost disciplines required to deploy AI agents safely at enterprise scale. The successful candidate will combine enterprise architecture leadership with hands-on expertise in AI agents, large language models, software development tooling, Model Context Protocol (MCP), cloud platforms, and regulated financial-services environments.

Key Responsibilities

1. Enterprise Agentic Delivery Lifecycle and Adoption Roadmap

• Establish and govern the ADLC for agent design, development, testing, approval, deployment, production access, and monitoring, supported by enterprise policies, standards, and reusable patterns.
• Define the adoption roadmap across Product Management, Software Engineering, and Platform Engineering, with risk-based controls and approval gates reflecting autonomy, data sensitivity, system access, financial impact, and regulation.

2. Agentic Platform and Infrastructure Architecture

• Architect, implement, and govern enterprise agentic platforms, including OpenAI Codex, Anthropic Claude Code, and approved alternatives, with secure model access, routing, identity, secrets, data protection, and privileged access.
• Design MCP and model gateways, execution environments, skill registries, and enterprise integrations, with end-to-end observability, auditability, performance, availability, and disaster-recovery standards.

3. Agent, Skill, and MCP Ecosystem Design

• Define standards for reusable agents, skills, workflows, orchestration, context, memory, tool usage, delegation, and human oversight.
• Architect and, where appropriate, develop MCP servers connecting models securely to enterprise applications, data, developer tools, and fintech APIs; maintain an approved registry and certification process for agents, skills, prompts, tools, and MCP services.

4. Token Economics and Cost Governance

• Establish budgets, quotas, alerts, allocation or chargeback models, and controls for enterprise agentic workloads.
• Monitor token, model, and infrastructure cost by agent, team, and use case; optimize model selection, context, caching, routing, and API usage, and partner with Finance and Procurement to forecast spend and measure return on investment.

5. Agentic AI Center of Excellence and Enablement

• Lead architecture for the Agentic AI Center of Excellence and publish reference architectures, playbooks, standards, and reusable patterns.
• Build training, architecture forums, communities of practice, and certification programs; advise teams and support pilots and production adoption while tracking productivity, quality, cost, and risk outcomes.

6. Governance, Risk, and Production Assurance

• Define governance for agent ownership, accountability, approval, production access, and ongoing operation.
• Establish controls for data leakage, prompt injection, hallucination, model misuse, unauthorized actions, and excessive autonomy, including monitoring, audit trails, rollback, and incident response; partner with Security, Legal, Privacy, Risk, Compliance, and architecture review boards.

7. Value Measurement and Continuous Improvement

• Define measures for adoption, productivity, cycle time, code quality, reliability, risk, cost, and employee experience.
• Review outcomes, remediate or retire underperforming and high-risk use cases, and update the roadmap as models, tools, standards, and market capabilities evolve.