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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 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 Responsibilities1\. 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.
Requirements* Significant enterprise, solution, or platform architecture experience, including standards, governance, design reviews, and technology roadmaps.
- Demonstrated experience designing and implementing enterprise AI, generative AI, or agentic AI platforms.
- Strong knowledge of large language models, agent orchestration, retrieval\-augmented generation, tool calling, context management, and human\-in\-the\-loop architectures.
- Hands\-on experience with agentic development tools such as OpenAI Codex, Anthropic Claude Code, GitHub Copilot, and with MCP servers, gateways, or comparable integrations.
- Strong knowledge of identity and access management, API security, secrets, data protection, observability, cloud architecture, CI/CD, and secure software delivery.
- Understanding of AI risk, model governance, privacy, regulatory compliance, and controls in regulated environments, with the ability to communicate clearly to executive, technical, risk, and business stakeholders.
Leadership and Behavioral Expectations
- Enterprise\-oriented and technically credible, balancing innovation with security, resilience, compliance, and cost discipline.
- Pragmatic and outcome\-focused, comfortable moving from executive strategy to detailed architecture and implementation.
- An influential communicator who builds alignment across Product, Engineering, Security, Risk, Compliance, Finance, and business leadership.
Measures of Success
- Adoption of a ADLC with focus on Product and Engineering, approved agentic platform, and supporting architecture and governance standards.
- Increasing use and reuse of certified agents, skills, MCP servers, and reference architectures through approved platforms, supported by trained practitioners.
- Measurable gains in delivery speed, quality, reliability, productivity, and 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. C-Level-level AI roles across all categories have a median of $250,000.
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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