SVP, Head of Agentic Engineering and Acceleration

Nuvei•Atlanta, GA

7d•Hybrid

About The Position

The SVP, Head of Agentic Engineering and Acceleration will lead the company's global transformation toward agentic software engineering and establish agentic development as a core enterprise capability. This executive will build and lead a specialized global engineering organization responsible for delivering production-grade software through agentic capabilities, establishing the company-wide Agentic Delivery Lifecycle (ADLC), and accelerating adoption across the broader product and technology organization. The organization will begin with approximately 10 to 15 highly skilled engineers, architects, platform specialists, and agentic-development leaders, and grow into a larger global engineering body as demand and demonstrated value increase. The team will operate as an agentic-native engineering organization. Approved agents, skills, workflows, and automated platforms will be used across the software lifecycle; conventional, manually intensive development will not be its standard delivery model. Engineers will direct, orchestrate, supervise, validate, and improve autonomous and semi-autonomous agents rather than primarily code through traditional methods. The team will also serve as the proving ground for the company's future engineering model, establishing the platforms, controls, practices, talent model, and reusable capabilities required to scale agentic-native development globally. The successful candidate will combine leadership of large, distributed technology organizations with deep expertise in software engineering, generative AI, agentic systems, platforms, security, and transformation. The role will collaborate closely with Product, Engineering, Enterprise Architecture, Platform Engineering, Information Security, Data, Risk, Compliance, Finance, and business AI leadership. It will support, but not own, the AI-powered product portfolio or AI adoption within nontechnology business functions.

Requirements

Significant executive experience leading global software engineering, AI engineering, developer platform, engineering transformation, or comparable technology organizations.
Demonstrated success building and leading high-performing teams across countries, cultures, time zones, and technology environments.
Proven experience driving enterprise-scale adoption of new engineering technologies, development methods, operating models, and organizational practices.
Demonstrated experience designing, building, or scaling enterprise generative-AI or agentic-AI platforms and production capabilities.
Deep understanding of modern software engineering, cloud platforms, developer experience, CI/CD, automated testing, source-code management, observability, security, and production operations.
Strong knowledge of large language models, agent orchestration, tool calling, retrieval-augmented generation, context and memory management, model routing, human oversight, MCP servers, model gateways, and enterprise APIs.
Practical experience with agentic development technologies such as OpenAI Codex, Anthropic Claude Code, GitHub Copilot, or comparable platforms.
Strong understanding of AI security, data protection, identity and access management, secrets management, model governance, intellectual-property protection, and responsible-AI controls.
Experience managing major technology investments, vendors, and global transformation programs; payments, fintech, financial services, or another highly regulated environment is strongly preferred.
Strong executive communication and influencing skills.
Bachelor's degree in computer science, engineering, information systems, or a related discipline, or equivalent experience; an advanced degree is preferred.

Nice To Haves

An advanced degree is preferred.

Responsibilities

Define and execute the multiyear strategy and roadmap for agentic product and engineering across the company's global technology organization.
Establish adoption objectives by engineering function, product domain, geography, technology stack, and maturity, moving teams from controlled experimentation to governed production use and agentic-first practices.
Partner with global product and engineering leaders to embed agentic capabilities into delivery models, processes, organizational structures, and accountabilities.
Identify and remove technical, organizational, cultural, talent, process, and governance barriers to adoption.
Establish executive governance and reporting covering adoption, investment, delivery outcomes, risk, cost, and realized business value.
Build and lead an initial team of approximately 10 to 15 engineers, architects, platform specialists, and agentic-development leaders, scaling it into a larger global engineering organization as value and demand grow.
Operate the team through an agentic-native model in which approved agents and workflows perform software design, development, testing, documentation, deployment, monitoring, maintenance, and modernization.
Ensure engineers primarily direct, orchestrate, supervise, validate, and optimize agents rather than rely on conventional manual software-development practices.
Deliver high-priority enterprise software, reusable components, platform capabilities, and modernization initiatives through this model.
Establish engagement, prioritization, delivery, talent, and leadership models, and codify successful practices into reusable standards, playbooks, architectures, agents, skills, and accelerators.
Own the design, implementation, governance, and continuous evolution of the company-wide ADLC.
Embed agentic capabilities throughout requirements, architecture, coding, review, testing, security validation, documentation, release, deployment, production operations, incident response, and modernization.
Define reusable patterns, control gates, certification, production-readiness standards, and risk-based requirements for human supervision, validation, approval, and intervention.
Integrate the ADLC with enterprise source-code management, CI/CD, testing, security, observability, change-management, and production-operations platforms.
Establish versioning, auditability, rollback, monitoring, incident-management, and lifecycle controls without compromising quality, resilience, maintainability, security, or regulatory compliance.
Define the requirements and target architecture for enterprise-grade agentic development and execution platforms, partnering with Platform Engineering, Enterprise Architecture, Security, and engineering leaders on implementation.
Lead adoption and integration of approved technologies such as OpenAI Codex, Anthropic Claude Code, GitHub Copilot, and comparable capabilities.
Provide secure, reliable, self-service access to approved models, tools, execution environments, enterprise data, repositories, APIs, golden paths, and reusable platform services.
Define requirements for model routing, context and memory, identity, secrets, privileged access, auditability, observability, availability, scalability, and disaster recovery; prevent fragmented tooling and ungoverned deployments.
Lead development of reusable enterprise agents, specialized skills, workflows, orchestration capabilities, and Model Context Protocol (MCP) services.
Establish an enterprise registry and standards for approved agents, skills, prompts, tools, MCP servers, context, memory, delegation, testing, versioning, ownership, and retirement.
Develop secure MCP servers and comparable integrations connecting models with enterprise applications, engineering platforms, data environments, operational tools, and core fintech APIs.
Establish certification, access, and reuse requirements that promote interoperability while preventing duplication, inconsistent practices, and uncontrolled agent proliferation.
Establish an acceleration capability that works directly with product and engineering organizations to identify and implement high-value agentic use cases.
Deploy embedded engineers into priority domains and lead lighthouse implementations that demonstrate value, transfer knowledge, and create sustainable local capability.
Develop training, technical academies, certifications, communities of practice, engineering forums, and a global network of agentic engineering champions.
Partner with engineering management to redefine roles, skills, team structures, workflows, career paths, and capacity assumptions as adoption matures.
Create implementation playbooks and change programs that support responsible experimentation, build confidence, address resistance, and sustain adoption across cultures and geographies.
Establish governance for ownership, approval, production access, operation, monitoring, and retirement of agents and agentic engineering capabilities.
Implement controls addressing data leakage, hallucination, prompt injection, insecure code generation, model misuse, unauthorized tool execution, intellectual-property exposure, and excessive autonomy.
Ensure production agents and agent-generated software have accountable owners, appropriate testing, audit trails, monitoring, rollback capabilities, and incident-management processes.
Partner with Information Security, Legal, Privacy, Risk, Compliance, and Internal Audit to meet regulatory and responsible-AI requirements, including clear exception, escalation, remediation, and risk-acceptance processes.
Define baselines, targets, dashboards, and executive reporting for productivity, cycle time, release frequency, quality, defect leakage, change-failure rate, reliability, modernization velocity, and developer experience.
Measure adoption and performance by team, geography, domain, workflow, and maturity, and compare the agentic-native organization with conventional delivery approaches.
Establish transparency and controls for token, model, licensing, infrastructure, and platform costs; optimize routing, context, caching, prompts, and platform utilization.
Remediate, consolidate, or retire underperforming and high-risk use cases, translating productivity gains into greater capacity, faster delivery, improved outcomes, and reduced cost.