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Vice President, Program Lead \- Agentic Software \& Product Development Life Cycle (ATOM)
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- New York, New York
- Operations Group
- 331044
Job Description
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About the Role:
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Grade Level (for internal use):
15
The Team:
The Enterprise Transformation team drives innovation across S\&P Global, leading strategic programs that reshape how the company operates and delivers products and services. We value collaboration, results\-driven execution, and cutting\-edge innovation as we transform the future of work through AI\-native capabilities. This team partners directly with stakeholders across all divisions and functions to deliver measurable impact at enterprise scale.
Responsibilities and Impact:
- Lead enterprise\-wide scaling of AI\-native PDLC and SDLC transformation across S\&P Global, directly impacting how the organization builds and delivers products
- Own end\-to\-end program strategy and execution roadmap from baseline assessment through implementation, influencing senior stakeholders across multiple divisions and the central technology and data function driving measurable business outcomes
- Manage program governance and reporting including providing updates to the Steering Committee and executive sponsor forums, and conducting workstream checkpoints and readiness reviews
- Prepare executive\-ready updates that frame solution options and go\-forward decisions clearly and enable sponsors to unblock issues quickly
- Collaborate with cross\-functional enterprise stakeholders and leaders on the future\-state operating model for agentic product and software delivery, clarifying how humans, AI agents, platforms, governance checkpoints, and cross\-functional teams interact across the lifecycle
- Build and run the enterprise coaching and enablement engine for agentic PDLC and SDLC adoption, including coach identification, onboarding, role expectations, ratios, coverage, practitioner communities, and contractor supply where needed
- Partner with key divisional leads to plan and implement training for tooling readiness, managing delivery constraints, optimizing division capacity, and managing coach\-resources availability for maximized impact
- Ensure adoption is embedded through applied coaching, product and engineering team\-level support, peer learning, and transition mechanisms that move teams from tool training to sustained agentic practice
- Drive value realization through measurable improvements in capacity, cycle\-time, product throughput, and timely reporting on operational efficiency in partnership with Finance and divisional leadership
- Identify risks, blockers, resource gaps, and execution slippage proactively, involving the relevant stakeholders to manage risks by having clear options for go\-forward decisions
- Partner with cross\-functional teams including change management, communications, people, finance, and product and technology leadership to define and implement a clear roadmap to orient and equip people leaders and teams on the new ways of working
S\&P Global states that the anticipated base salary range for this position is $220,000 to $350,000\. Final base salary for this role will be based on the individual’s geographic location, as well as experience level, skill set, training, licenses and certifications.
In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan.
This role is eligible to receive additional S\&P Global benefits. For more information on the benefits we provide to our employees, .
Include for roles that are bonus plan eligible, including sales commission plans.
What We're Looking For:
Basic Required Qualifications:
- 15\+ years of experience in technology transformation, program leadership, or engineering operations with demonstrated ownership of large\-scale, multi\-division change initiatives
- Proven track record delivering product development and engineering transformations at enterprise scale, with executive\-level visibility and stakeholder management
- Demonstrated ability to influence and collaborate with C\-level executives, CTOs, and senior business leaders across multiple functions
- Strong problem\-solving skills and the ability to blend innovative thinking with practical understanding of execution realities
- Deep understanding of modern software delivery lifecycles, including agile methodologies, Dev SecOps practices, and AI\-assisted engineering workflows
- Experience operating in regulated environments with deep understanding of control, risk, compliance, and audit requirements
- Bachelor's degree in Engineering, Computer Science, Business, or equivalent combination of education and experience. Master’s degree is a plus
Additional Preferred Qualifications:
- Hands\-on background in engineering, product management, or technical delivery before transitioning to enterprise transformation leadership
- Experience designing and implementing enterprise coaching models, capability academies, or scaled adoption programs for technology transformations
- Familiarity with AI\-enabled development tools and platforms such as GitHub Copilot, automated testing frameworks, or multi\-agent workflow systems
- Consulting or transformation office experience with structured problem\-solving methodologies and enterprise PMO disciplines
What’s In It For You?
Our Mission:
Advancing Essential Intelligence.
Our People:
We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all.From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it. We are changing the way people see things and empowering them to make an impact on the world we live in. We’re committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.
Our Values:
Integrity, Discovery, Partnership
Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals.
Benefits:
We take care of you, so you can take care of business. We care about our people. That’s why we provide everything you—and your career—need to thrive at S\&P Global.
Our benefits include:
- Health \& Wellness: Health care coverage designed for the mind and body.
- Flexible Downtime: Generous time off helps keep you energized for your time on.
- Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
- Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company\-matched student loan contribution, and financial wellness programs.
- Family Friendly Perks: It’s not just about you. S\&P Global has perks for your partners and little ones, too, with some best\-in class benefits for families.
- Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.
For more information on benefits by country visit: https://spgbenefits.com/benefit\-summaries
Global Hiring and Opportunity at S\&P Global:
At S\&P Global, we are committed to fostering a connected and engaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets.
Recruitment Fraud Alert:
If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to [email protected]. S\&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre\-employment training” or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here.
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Equal Opportunity Employer
S\&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment.
If you need an accommodation during the application process due to a disability, please send an email to: [email protected] and your request will be forwarded to the appropriate person.
US Candidates Only: Know Your Rights: Workplace discrimination is illegal
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102 \- Senior Management (EEO Job Group) (inactive), 10 \- Officials or Managers (EEO\-2 Job Categories\-United States of America), OPRTON102 \- Senior Management (EEO Job Group)
Job ID: 331044
Posted On: 2026\-08\-16
Location: New York, New York, United States
Salary Context
This $220K-$350K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 S&P Global, 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 in Demand for This Role
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. This role's midpoint ($285K) sits 33% above the category median. Disclosed range: $220K to $350K.
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.
S&P Global AI Hiring
S&P Global has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $180K - $350K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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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