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.
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