About the Role: Grade Level (for internal use): 13 S&P Global Energy The Role - Director, AI-Driven Applications The Team: We are looking for a highly motivated, enthusiastic, and skilled engineering leader for S&P Global Energy.
We strive to deliver solutions that are sector-specific, data-rich, and hyper-targeted for evolving business needs. The Impact: S&P Global Energy is seeking a Director of AI-Driven Applications who is a senior strategic and hands-on technical leadership role responsible for defining and executing the organization''''s vision for intelligent, AI-native enterprise systems. This role requires deep expertise in agentic AI design, large-scale data modelling, and enterprise solution architecture translating complex business challenges into scalable, production-grade AI solutions.
Responsibilities: AI Strategy & Application Development Define the enterprise AI application strategy, roadmap, and governance frameworks aligned with business objectives Lead the design and delivery of production AI applications using LLMs, multi-agent systems, retrieval-augmented generation (RAG), and fine-tuning pipelines Architect end-to-end agentic AI workflows: autonomous planning, tool use, memory management, and multi-agent orchestration Evaluate and select AI frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, custom) appropriate to enterprise scale and security requirements Champion responsible AI principles - explainability, bias mitigation, auditability, and data privacy Data Modelling & Architecture Design and govern enterprise data models supporting AI feature engineering, vector embedding stores, knowledge graphs, and real-time inference pipelines Define ontologies, taxonomies, and semantic layers that enable AI systems to reason over structured and unstructured enterprise data Oversee the architecture of data platforms including data lakes, lakehouses, streaming pipelines, and feature stores Establish data quality, lineage, and observability standards to ensure model reliability in production Enterprise Architecture Leadership Own and evolve the enterprise architecture for AI integration across cloud, hybrid, and on-premise environments Drive architecture decisions across the full AI stack: model serving, API gateways, orchestration layers, observability, and security Align AI architecture with enterprise platforms and integration patterns (event-driven, microservices, API-first) Establish architecture review boards, design patterns, and reusable accelerators to scale AI delivery across business units People Leadership & Stakeholder Engagement Build, mentor, and lead a high-performing team of AI and Software engineers, data architects, and solution architects Partner with C-suite, business unit heads, and product leaders to identify and prioritize AI opportunities Represent the AI architecture function to executive stakeholders, board-level audiences, and external partners Drive a culture of continuous learning, experimentation, and engineering excellence Success Metrics (First 12 Months) Deliver an enterprise AI application roadmap adopted by executive leadership Ship at least two flagship agentic AI applications to production at enterprise scale Establish reusable AI architecture patterns and accelerators reducing delivery time by 30%+ Build and grow a team of AI engineers and architects Define, publish, evangelize enterprise AI governance and data modelling standards Basic Required Qualifications 12+ years of progressive experience in software/data/AI architecture with 5+ years in a leadership role Proven delivery of enterprise-scale AI or ML applications in production environments Deep expertise in at least two major cloud platforms (AWS, Azure, GCP) and associated AI/ML services Strong background in data modelling and data platform design (relational, NoSQL, graph, vector) Demonstrated experience architecting agentic AI solutions using LLMs and orchestration frameworks Excellence in stakeholder communication, able to translate technical concepts for executive audiences Additional
Preferred Qualifications: Experience in regulated industries (financial services, healthcare, government) with AI governance requirements Published research, patents, or open-source contributions in AI/ML Certifications: AWS Solutions Architect Professional, Google Cloud Professional Data Engineer, or equivalent Experience with TOGAF, Zachman, or other enterprise architecture frameworks