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About This Role
About the Role:
Grade Level (for internal use):
13 The Team:
The Director of AI Solutions & Integration leads the team that brings advanced analytics and AI capabilities into the heart of SPDJI's data operations. Reporting to the Director of Data AI & Enablement, this role ensures that AI is not just an experiment, but a production-ready, governed, and value-driving capability. This strategic leadership position enables SPDJI to harness the power of AI at scale, by accelerating innovation, enhancing productivity, and ensuring that every AI solution is safe, and aligned with business priorities.
Responsibilities and Impact:
Strategic Leadership & AI Vision
- Own and drive the AI solutions enablement strategy for SPDJI, defining how value streams design, build, and scale AI applications that deliver measurable business value
- Establish reference architectures and standards for GenAI/LLM solutions (e.g., RAG patterns, agentic workflows, orchestration, integration into data pipelines), ensuring solutions are secure, governable, and production-ready
- Define and implement enablement programs for GenAI, LLM, and agent-based solutions across the organization
- Contribute to the broader Data AI & Enablement strategy, ensuring AI capabilities align with organizational strategic goals and data platform initiatives
- Represent SPDJI Data at enterprise AI forums, advocating for AI capabilities and breakthrough innovations
AI Roadmap & Strategic Planning
- Partner with PPD and value stream leadership to shape the AI roadmap, provide feasibility input, identify dependencies, and define clear success metrics and acceptance criteria for prioritized use cases
- Collaborate with stakeholders to identify high-value AI opportunities across Equity, Fixed Income, and Multi-Asset domains
- Balance innovation with pragmatism, ensuring AI investments deliver tangible business outcomes
- Provide technical leadership on emerging AI technologies and their applicability to index management and data operations
Delivery Through Enablement
- Co-develop AI applications with value stream SMEs, from prototyping to production deployment
- Establish prompt engineering and evaluation practices including reusable prompt patterns, test harnesses, quality benchmarks, and regression approaches to prevent degradation over time
- Oversee the design and implementation of end-to-end AI solutions including RAG pipelines, agentic workflows, and LLM-enabled applications
Governance & Responsible AI
- Partner closely with the Data & AI Governance team to embed responsible AI practices and risk controls into every solution
- Ensure all AI solutions are integrated with governed data, meet security and operational requirements
- Establish and maintain frameworks for AI solution monitoring, evaluation, and continuous improvement
Production Readiness & IT Partnership
- Ensure operational readiness for IT handover by driving standards for documentation, monitoring, cost controls, incident response expectations, and secure integration with enterprise platforms
- Partner with IT to support QA processes and early production stabilization
- Establish quality gates and "definition of done" criteria specific to AI solutions
Team Development & Capability Building
- Lead, mentor, and develop a high-performing team of AI Solutions Leads and Experts
- Develop team capability and reusable assets (starter kits, libraries, templates, office hours/workshops), accelerating adoption of AI patterns across the organization
- Collaborate effectively with Data Integration & Workflows and Data Governance teams to ensure cohesive platform enablement
Shared Accountabilities
- With PPD: Collaborate on AI roadmap prioritization and alignment with business requirements and strategic goals; provide realistic technical feasibility assessments and success metrics definition.
- With IT: Partner to ensure infrastructure readiness for AI workloads, smooth deployment of production-ready AI solutions, and operational excellence; establish clear support boundaries and SLAs.
- With Data Value Streams: Engage with value stream SMEs to co-develop AI solutions, ensuring alignment with business logic and domain expertise; assess and develop SME AI capabilities.
- With Data & AI Governance: Collaborate closely to embed responsible AI controls, conduct safety reviews, and ensure compliance with AI usage policies and risk management frameworks.
- With Data Integration & Workflows: Partner to operationalize AI data requirements, refresh cadences, and integration of AI solutions with data pipelines.
Ownership
- AI Solutions Strategy: Own the technical strategy, standards, and execution approach for all AI and advanced analytics initiatives
- AI Reference Architectures: Responsible for defining and maintaining reference architectures for GenAI, LLM, RAG, and agentic solutions
- AI Production Readiness Framework: Define and enforce the "definition of done" for production-ready AI solutions
What Success Looks Like
- Enable AI at Scale: Establish scalable patterns and frameworks that enable multiple value streams to deliver AI solutions efficiently and consistently
- Ensure Responsible AI: Embed governance, security, and ethical considerations into every AI solution, maintaining organizational trust and compliance
- Accelerate AI Innovation: Reduce time-to-value for AI use cases through reusable components, clear standards, and effective enablement
- Drive Measurable Business Value: Ensure AI solutions deliver quantifiable business outcomes aligned with strategic priorities
Key Performance Indicators (KPIs)
- Business Value Realization: Measurable business impact (productivity gains, cost savings, quality improvements) from deployed AI solutions
- Production Stability: Incident rate, performance metrics, and cost efficiency of AI solutions in production
- Time to Production: Reduction in time from AI prototype to production-ready solutions
Compensation/Benefits Information: (This section is only applicable to US candidates)
S&P Global states that the anticipated base salary range for this position is $149,031 to $228,996. 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 eligible to receive additional S&P Global benefits.
What We’re Looking For:
Basic Required Qualifications:
Education & Experience
- Bachelor's degree in Computer Science, Data Science, Engineering, or related field; Master's degree or PhD in AI/ML-related field preferred
- 10+ years of experience in AI/ML, data science, or advanced analytics roles
- 5+ years of leadership experience managing technical teams and delivering AI/ML solutions at scale
- Proven track record of building and deploying production AI systems in complex enterprise environments
- Experience in financial services or quantitative modeling preferred
Technical Expertise
- Deep expertise in ETL/ELT design patterns, data pipeline architecture, and workflow orchestration framework
- Strong knowledge of both batch and streaming data processing technologies
- Experience with modern data platforms, cloud infrastructure (AWS, Azure, or GCP), and data engineering tools
- Proficiency in programming languages commonly used in data engineering (Python, SQL, Scala, etc.)
- Understanding of data quality frameworks, observability, monitoring, and alerting systems
- Knowledge of DevOps practices, CI/CD pipelines, and infrastructure-as-code
- Familiarity with data governance principles and compliance frameworks
Leadership & Soft Skills
- Strong leadership and team management skills with ability to inspire, mentor, and develop technical talent in emerging technologies
- Excellent collaboration skills with ability to partner effectively across technical and business teams in a matrixed organization
- Outstanding communication abilities, able to articulate complex AI concepts to non-technical audiences and translate business problems into AI solutions
- Strategic thinking with ability to align AI initiatives with business objectives and drive innovation
- Strong problem-solving mindset with focus on practical, production-ready solutions
Preferred Qualifications:
- Experience across the breadth of index concepts and asset classes
- Familiarity with SPDJI products, methodologies, or index calculation processes
- Experience in Python or SQL Development
- Experience in AI/ML technologies or cloud AI platforms
- Experience with Agile/Scrum methodologies and product-oriented delivery models
- Knowledge of data governance, data quality frameworks, and enterprise data architecture
- Experience building AI solutions for financial data, market data, or analytical workflows
Right to Work Requirements:
This role is limited to persons with indefinite right to work in the United States.
We require all external candidates who reach the final stage of our interview process to attend at least one in-person interview, which is ordinarily at your nearest S&P Global office. This must be completed before we can proceed to an offer.
About S&P Global Dow Jones Indic e s
At S&P Dow Jones Indices, we provide iconic and innovative index solutions backed by unparalleled expertise across the asset-class spectrum. By bringing transparency to the global capital markets, we empower investors everywhere to make decisions with conviction. We’re the largest global resource for index-based concepts, data and research, and home to iconic financial market indicators, such as the S&P 500 ® and the Dow Jones Industrial Average ® . More assets are invested in products based upon our indices than any other index provider in the world. With over USD 7.4 trillion in passively managed assets linked to our indices and over USD 11.3 trillion benchmarked to our indices, our solutions are widely considered indispensable in tracking market performance, evaluating portfolios and developing investment strategies.
S&P Dow Jones Indices is a division of S&P Global (NYSE: SPGI). S&P Global is the world’s foremost provider of credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity and automotive markets. With every one of our offerings, we help many of the world’s leading organizations navigate the economic landscape so they can plan for tomorrow, today. For more information, visit www.spglobal.com/spdji .
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: The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf
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20 - Professional (EEO-2 Job Categories-United States of America), ANLYTC202.2 - Middle Professional Tier II (EEO Job Group), SWP Priority – Ratings - (Strategic Workforce Planning)
Salary Context
This $149K-$228K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($189K) sits 11% below the category median. Disclosed range: $149K to $228K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
S&P Global AI Hiring
S&P Global has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Princeton, NJ, New York, NY, US, New York, NY. Compensation range: $228K - $350K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% above the national 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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