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About This Role
What We Need
Corpay is currently looking to hire an AI Product Delivery Excellence Manager within our Payables division. In this role, the AI Product Delivery Excellence Manager is a key driver of product delivery excellence, helping Product teams and leaders adopt and continuously improve modern, AI-driven planning, discovery, prioritization, and delivery readiness practices.
The role combines strategic product delivery leadership with operational rigor to ensure teams define, prioritize, and prepare high-quality work aligned with business priorities in a predictable and efficient way, while effectively leveraging AI tools.
How We Work
As an AI Product Delivery Excellence Manager, you will be expected to work in a virtual environment. Corpay will set you up for success by providing:
- Company-issued equipment
- Formal, hands-on training
Role Responsibilities
The responsibilities of the role will include:
- Optimizing the full product delivery lifecycle from intake and discovery through prioritization and ready-for-development handoff
- Improving flow efficiency by identifying and removing bottlenecks across Product workflows and systems
- Shifting teams toward continuous planning and outcome-based execution
- Balancing speed, quality, and predictability in product readiness and delivery inputs
- Owning discovery predictability and operational health across Product teams
- Increasing visibility of cross-team dependencies, prioritization risks, and handoff issues while driving mitigation with stakeholders
- Maintaining stable Product operations through scope discipline, clear operating rhythms, and consistent decision-making
- Driving adoption of AI-driven product and delivery practices
- Leveraging AI tools and team analytics for intake planning, prioritization, portfolio visibility, and delivery tracking
- Identifying risks and opportunities through data-driven insights
- Continuously evaluating and adopting emerging AI capabilities to improve Product effectiveness and delivery readiness
- Partnering with Product, Engineering, and stakeholders to align planning and delivery with strategic objectives
- Collaborating with Engineering to balance AI-DLC initiatives, product priorities, and operational delivery needs
- Communicating progress, risks, and outcomes clearly and consistently
- Partnering with leaders and stakeholders to evolve Product practices and operating models
- Defining, tracking, and reporting key Product and shared delivery metrics
- Providing actionable insights to inform prioritization and delivery decisions
- Monitoring Product operating performance and proactively addressing deviations
- Promoting adherence to quality standards, planning discipline, and Product best practices
- Maintaining consistent Product artifacts, discovery processes, and handoff standards
- Supporting governance, compliance, and audit readiness where required
- Leading continuous improvement using agile and flow-based principles adapted to AI-driven development
- Running experiments to improve team effectiveness and Product operating outcomes
- Standardizing best practices across teams while enabling local adaptability
- Coaching teams on AI-DLC, Product operating principles, and evolving ways of working
- Enabling adoption of new tools and operating practices through facilitation, coaching, and hands-on support
- Fostering high-performing, accountable, and self-organizing teams
- Promoting a culture of continuous learning, innovation, and ownership
Qualifications & Skills
- 5+ years of experience in Product Operations, Product Delivery, Agile Delivery, Program Management, or similar roles
- Bachelor's degree in Business, Technology, or a related field, or equivalent practical experience
- Experience working with cross-functional Product and engineering teams
- Proven track record of improving planning, delivery readiness, and operating predictability
- Experience in fast-moving, technology-driven environments
- Experience leading adoption of new technologies, operating practices, or delivery models
- Demonstrated expertise with Jira or similar tooling, including administration, workflow design, custom fields, and reporting
- Experience with Product and delivery metrics, reporting, and analytics
- Hands-on experience with AI-assisted development or Product tools such as GitHub Copilot, Cursor, ChatGPT, or similar platforms
- Understanding of AI-DLC, agile delivery, and flow-based operating models
- Strong analytical thinking and problem-solving skills
- Ability to interpret and act on data-driven insights
- Excellent communication skills
- Demonstrated change leadership and coaching capability
Preferred Qualifications
- Experience in organizations actively scaling AI-assisted software or product delivery
- Experience formalizing operating governance in regulated or audit-sensitive environments
- Experience coaching teams through operating model transitions
- Familiarity with AI workflow tools, prompt-based process automation, or LLM-assisted Product tooling
- Scrum Master, SAFe, or Agile coaching certification (as a foundation, not a requirement to apply Scrum specifically)
- Experience in a FinTech, SaaS, or B2B product environment
- Experience with product analytics or BI tooling (e.g., Tableau, Looker, Power BI)
Benefits & Perks
- Comprehensive Benefits Package including, Medical, Dental, Vision and Paramedical benefits, as of day one
- Optional company match RRSP program
- Virtual fitness classes offered company-wide
- Time-off including major holidays, vacation, sick, personal, & volunteer time
- Discounted gym membership rate
- Philanthropic support with both local and national organizations
- Fun culture with company-wide contests and prizes
About Corpay
Corpay is a global technology organization that is leading the future of commercial payments with a culture of innovation that drives us to constantly create new and better ways to pay. Our specialized payment solutions help businesses control, simplify, and secure payment for fuel, general payables, toll and lodging expenses. Millions of people in over 80 countries around the world use our solutions for their payments.
At Corpay, we are committed to fostering an inclusive and respectful workplace where employees are valued for their diverse perspectives, experiences, and contributions. We believe that diversity, equity, and inclusion strengthen our teams, drive innovation, and support our continued success globally.
As part of our hiring process, offers of employment may be subject to the successful completion of pre-employment screening conducted by an authorized third-party provider, in accordance with applicable laws and Corpay policies. Screening requirements may include employment references, identity verification, criminal record checks, financial or sanctions screening, and other background checks relevant to the role and permitted by local law.
Notice to Recruitment Agencies and Search Firms: Corpay does not accept unsolicited resumes from agencies or search firms without a valid written agreement in place. Any unsolicited candidate submissions will become the property of Corpay, and no fees will be paid related to such submissions.
Learn more about Corpay: https://www.corpay.com
Transparency & Compliance
Equal Opportunity Employer
Corpay is committed to providing equal employment opportunities to all applicants and employees. Employment decisions are made without regard to race, color, religion, sex (including pregnancy), gender, gender identity or expression, sexual orientation, national origin, ancestry, age, disability, marital status, genetic information, military or veteran status, or any other characteristic protected by applicable law. Corpay is committed to fostering an inclusive workplace where individuals are respected and valued for their diverse perspectives, experiences, and contributions. If you require reasonable accommodation during any part of the application or interview process, please notify a representative of the Human Resources Department.
Use of Artificial Intelligence in Hiring
Corpay may use artificial intelligence (AI) and other technology-enabled tools to support certain aspects of the recruitment process, such as application screening, candidate assessment, or interview scheduling. These tools are designed to enhance efficiency, consistency, and fairness throughout the hiring process. AI tools do not make final hiring decisions. All employment decisions involve human review. Corpay is committed to the responsible use of AI, including appropriate oversight and safeguards designed to support fair and unbiased outcomes.
Candidate Privacy Notice
For information about how Corpay processes personal information during the recruitment process, please review our Candidate Privacy Notice: https://www.corpay.com/privacy-policy.
Pay Philosophy
Corpay is committed to fair, equitable, and transparent compensation practices. Compensation decisions are based on objective, job-related factors including skills, experience, qualifications, and market benchmarks. Where required by applicable law, salary or compensation ranges will be included in the job posting or provided prior to the interview process, where required by applicable law. Additional compensation elements such as bonuses, incentives, benefits, or variable pay may apply where applicable.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Corpay, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Corpay AI Hiring
Corpay has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Positions span Peachtree Corners, GA, US, Atlanta, GA, US, Atlanta, GA.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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