This role pays more than
77%
of similar roles. Most pay
$183,368–$266,812
— the shaded band above.
At the midpoint, this role pays about
$272k
versus about
$225k
for comparable roles.
Based on 240 similar postings.
Employer
About S&P Global
S&P Global delivers Essential Intelligence® that shapes decision making. We provide the world’s leading organizations with the right data, connected technologies and expertise they need to move ahead.
S&P Global currently has
49 open roles
on FindRole.
Listed pay typically runs
$140,000–$200,000
across 41 roles with salary data.
As the Global Head of Agentic Engineering, you will lead a pioneering team within S&P Dow Jones Indices Technology to develop autonomous and semi-autonomous agent systems. You will define the global framework for agentic engineering, including reference architectures, orchestration patterns, and evaluation systems while transitioning traditional software development into spec-driven processes. Your daily work involves building automated quality engineering capabilities, establishing governance for agentic systems in regulated environments, and leading a high-performing team of engineers. The role requires deep expertise in LLM-based agents, LangChain, AutoGen, retrieval systems, and CI/CD pipelines like Jenkins or GitLab CI. You will solve complex problems regarding the integration of AI agents into the software development lifecycle to ensure reliability and traceability within the highly regulated domain of global benchmark administration and financial market data.
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Machine Learning
Natural Language Processing
Reasoning Systems
RLHF
RLAIF
Distributed Systems
Chain-of-Thought
Multi-agent Systems
Orchestration Frameworks
Evaluation Frameworks
Observability
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Machine Learning
Natural Language Processing
Distributed Systems
RLHF
RLAIF
Chain-of-Thought
Multi-agent Systems
Observability
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Machine Learning
Natural Language Processing
Reasoning Systems
RLHF
RLAIF
Distributed Systems
Chain-of-Thought
Multi-agent Systems
Orchestration Frameworks
Evaluation Frameworks
Observability
Agentic AI
Large Language Models
Retrieval-Augmented Generation (RAG)
Reasoning Systems
Machine Learning
Natural Language Processing
RLHF
RLAIF
Distributed Systems
Multi-agent Systems
Orchestration Frameworks
Chain-of-Thought
Model Alignment
Evaluation Frameworks
Observability