About the Role
The Team:
You will join S&P Global Ratings' Data Services Team — an expert-led group at the forefront of AI/ML and Generative AI innovation, bringing deep expertise across critical Ratings data domains, technology platforms, and architectural patterns. The team operates with a clear mandate: to deliver the next generation of S&P Ratings' analytics infrastructure through technical rigor, collaborative innovation, and the translation of cutting-edge AI research into reliable, production-grade systems. This is a unique opportunity to help architect and shape the AI capabilities that inform credit decisions relied upon by global markets every day.
Responsibilities and Impact:
•Architect and own the end-to-end delivery of scalable ML and Generative AI solutions — from requirements definition with senior business stakeholders through to production deployment on AWS — directly shaping S&P Ratings' next-generation analytics platform.
•Design and build reusable AI platforms, model evaluation frameworks, and performance optimization tooling used across the Ratings organization, establishing the technical standards that power enterprise-wide innovation.
•Apply state-of-the-art GenAI technologies — including agentic AI frameworks, LLM fine-tuning, and retrieval-augmented generation — to complex credit ratings datasets, producing data-driven insights that directly inform global market decisions.
•Lead technical research and adoption of emerging AI/ML technologies, driving a measurable improvement in the quality, scalability, and performance of existing Ratings analytics solutions.
•Partner with cross-functional teams of data scientists, software engineers, and business analysts to translate complex analytical requirements into high-performance, production-grade solutions delivered within agreed timelines.
•Define and champion AI engineering best practices, reusable capability frameworks, and technical standards that will govern the Data Services team's contribution to S&P Global Ratings for years to come.
What We're Looking For — Basic Required Qualifications:
•Cloud AI/ML Experience: 10+ years of experience designing, building, and deploying production-grade AI, ML, or data-driven solutions in AWS cloud environments (e.g., SageMaker, Bedrock, or Glue), with end-to-end ownership from solution architecture through live operation.
•Python Programming & ML Frameworks: 5+ years of Python development with production application of at least one ML framework (TensorFlow, PyTorch, or scikit-learn) within end-to-end model training, evaluation, and deployment pipelines.
•Java Engineering & Cloud Infrastructure: 3+ years of Java application development using Spring Boot in microservices architectures, combined with 3+ years deploying and managing containerized workloads using Docker and Kubernetes in cloud-hosted environments, with active use of Git-based CI/CD workflows.
•Production AI Systems — GenAI, NLP & Data: Demonstrated delivery of at least 2 production AI systems incorporating deep learning, NLP, or Generative AI techniques, with applied SQL-based management of structured and unstructured datasets at enterprise scale, and hands-on development using agentic AI frameworks or autonomous workflow architectures.
•AI-Assisted Development Tooling: Demonstrated use of AI-assisted development tools (GitHub Copilot, Cursor, or Claude Code) across at least 2 shipped production projects, integrated within a structured team-based software development lifecycle.
Additional Preferred Qualifications:
•Delivered at least 1 production or near-production agentic AI system using LangChain, LangGraph, CrewAI, ADK, or Strands.
•2+ years of hands-on experience implementing graph databases (e.g., Neo4j, Amazon Neptune) or graph-based algorithms within data engineering or ML feature pipelines.
•Working proficiency in at least one front-end technology (React, JavaScript, or HTML) demonstrated through integration of ML model outputs into a user-facing application.
•2+ years of data engineering experience including pipeline development, feature engineering, and data preparation for ML model training in a cloud environment.
•Hands-on experience designing or operating data lakes or AI-ready data platforms (e.g., AWS Lake Formation, Databricks, or Snowflake) at enterprise scale.
•Experience deploying ML solutions using Apache Spark or Hadoop on datasets exceeding 1TB in a distributed cloud environment.
•2+ years applying AI-native SDLC practices including automated model evaluation pipelines, experiment tracking (e.g., MLflow or Weights & Biases), and model versioning within a team-based CI/CD workflow.
Work Arrangement: This is a hybrid role requiring a minimum of 2 days per week in the New York, NY office. Specific in-office days are agreed with your hiring manager to support team collaboration and delivery.
Right to Work Requirements: This role is limited to persons with indefinite right to work in the United States.
Compensation/Benefits Information (US Applicants Only):
S&P Global states that the anticipated base salary range for this position is $149,302 – $209,000. Final base salary for this role will be based on the individual's geographical location as well as experience and qualifications for the role. In addition to base compensation, this role is eligible for an annual incentive plan. This role is not eligible for additional compensation such as an annual incentive bonus or sales commission plan. This role is eligible to receive additional S&P Global benefits.
About S&P Global Ratings:
At S&P Global Ratings, our analyst-driven credit ratings, research, and sustainable finance opinions provide critical insights that are essential to translating complexity into clarity so market participants can uncover opportunities and make decisions with conviction. S&P Global Ratings is a division of S&P Global (NYSE: SPGI), the world's foremost provider of credit ratings, benchmarks, analytics and workflow solutions in the global capital, commodity and automotive markets.
Benefits include:
•Health & Wellness: Health care coverage designed for the mind and body.
•Flexible Downtime: Generous time off.
•Continuous Learning: Access to resources to grow your career.
•Invest in Your Future: Competitive pay, retirement planning, continuing education program with company-matched student loan contribution, and financial wellness programs.
•Family Friendly Perks: Benefits for partners and children.
•Beyond the Basics: Retail discounts and referral incentive awards.
