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About This Role
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 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.
Key Responsibilities
1\. Agentic Strategy and Global Adoption
- 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.
2\. Agentic\-Native Engineering Organization
- 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.
3\. Agentic Delivery Lifecycle
- 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.
4\. Agentic Platforms and Developer Experience
- 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.
5\. Agents, Skills, and MCP Ecosystem
- 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.
6\. Engineering Adoption and Transformation
- 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.
7\. Governance, Security, and Production Assurance
- 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.
8\. Value Realization and Cost Management
- 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.
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.
Leadership Expectations
- A transformational global leader who converts an ambitious vision into disciplined execution and measurable outcomes.
- Technically credible from executive strategy through detailed engineering, architecture, platform, security, and control decisions.
- An organizational builder who attracts specialized talent, develops leaders, and creates clarity and accountability across distributed teams.
- Pragmatic, data\-driven, and commercially minded, balancing velocity with quality, security, resilience, compliance, and cost.
- An influential change leader who challenges traditional practices, builds confidence, manages resistance, and sustains adoption across cultures and geographies.
Measures of Success
Success in this role will be measured by:
- Establishment and enterprise adoption of a governed Agentic Delivery Lifecycle and approved agentic engineering platforms.
- Formation and effective operation of the initial 10\-to\-15\-person agentic\-native team, followed by disciplined growth into a larger global capability.
- Delivery of secure, reliable, production\-grade software through an agentic\-native delivery model that does not rely on traditional manual development as its standard approach.
- Measurable improvement in delivery speed, quality, reliability, modernization velocity, engineering capacity, developer experience, and cost compared with conventional methods.
- Progression of engineering teams from experimentation to repeatable, governed, production\-scale adoption.
- Adoption and reuse of certified agents, skills, MCP servers, workflows, platforms, and engineering accelerators, with reduced duplication and manual activity.
- Effective management of agentic risk and model, token, platform, licensing, infrastructure, and operating costs, with demonstrated business value.
Benefits
- Competitive holiday allowance
- 401K Matching program
- Group Insurance Benefits
- Flexible working model
- Employee Assistance Program
Nuvei is an equal\-opportunity employer that celebrates collaboration and innovation and is committed to developing a diverse and inclusive workplace. The team at Nuvei is comprised of a wealth of talent, skill, and ambition. We believe that employees are happiest when they’re empowered to be their true, authentic selves. So, please come as you are. We can’t wait to meet you.
Working Language
English (written and spoken) is the language used most of the time, as work colleagues, clients, and strategic suppliers are geographically dispersed.
Our recruitment process may use automated tools, including AI, to support application management and candidate shortlisting.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Nuvei, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Nuvei AI Hiring
Nuvei has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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