Global Head of Agentic Engineering

Responsibilities and Impact:
Define and deliver the global agentic engineering framework for S&P DJI Technology, including reference architecture, orchestration patterns, agent registries, and evaluation systems that engineering teams will adopt across the organization

Lead the transformation from traditional software development to agent-driven processes, establishing spec-driven development where requirements flow seamlessly from product definition through implementation to verification with full traceability

Build and implement agentic quality engineering capabilities, creating automated code quality assessment, test generation and maintenance tooling, and continuously self-maintaining test suites that enhance product reliability

Partner with Risk, Compliance, Information Security, and Internal Audit to establish governance frameworks for agentic systems in a regulated benchmark environment, ensuring proper model governance and human-in-the-loop controls

Drive organizational change through influence and collaboration, earning trust from engineering leaders and translating complex technical capabilities into business value for non-technical stakeholders including executive leadership

Build and lead a high-performing team of engineers while raising agentic fluency across the wider S&P DJI Technology organization through enablement, standards, and hands-on partnership

What We're Looking For:
Basic Required Qualifications:
Deep, hands-on expertise in agentic technologies including LLM-based agents, orchestration frameworks such as LangChain or AutoGen, tool integration, context engineering, and retrieval systems with proven experience building these systems at scale

Demonstrated experience applying AI agents to software development lifecycle processes including code generation and review, automated test creation, or autonomous quality assurance in production environments

Strong software and quality engineering foundations with expertise in test architecture, CI/CD pipelines such as Jenkins or GitLab CI, static and dynamic analysis tools, and performance engineering practices

Proven track record of building and leading engineering teams, including hiring senior talent and successfully driving organizational transformation across multiple teams

Executive-level communication skills with ability to influence technical and non-technical stakeholders, including CTOs, risk committees, and senior engineering leadership

Clear judgment on appropriate use cases for autonomous agents versus human oversight, with experience in risk assessment and mitigation strategies for AI systems

Additional Preferred Qualifications:
Experience in financial services, market data, index administration, or other regulated, high-accuracy domains with understanding of compliance and audit requirements

Familiarity with data quality and validation frameworks at enterprise scale, including experience with data governance and correctness verification in mission-critical systems

Background in AI risk management frameworks, model risk management practices, or governance structures for autonomous systems in regulated environments

Active participation in the open-source community through contributions to agentic frameworks such as LangChain or CrewAI, publications, or recognized technical presence in developer tooling communities