Job Description
About the Role: Grade Level (for internal use): 12 About the Role: We are seeking an exceptional Principal Machine Learning Engineer to join our organization at the forefront of applied AI. This is a senior individual contributor role designed for a practitioner who is equally at home architecting large-scale LLM infrastructure, building scalable python backend APIs, and driving organization wide AI transformation. You will be designing and delivering Generative AI and Agentic AI systems, setting engineering standards for building production grade ML applications, and mentor engineering teams across the organization. You will play a critical role in leading S&P's AI-driven transformation to drive value internally and for our customers. The Team: You will work closely as part of a world-class AI & ML team comprised of Data Science, Machine Learning, MLOps and Data Engineers. The Agentic AI Platform & ML Engineering team is the central nervous system of our AI capabilities - a high-impact group working at the intersection of cutting-edge research and real-world product delivery. We operate with a strong engineering culture that moves fast without sacrificing quality, treats AI systems with mission-critical rigor, and obsesses over operationalization because models that aren't in production aren't delivering value. Responsibilities and Impact: LLM & Generative AI Engineering: Deploy and architect production-scale LLM systems spanning frontier models (GPT-4 class), open-source variants (such as LLaMA, Mistral, Gemma), RAG pipelines, and multi-modal AI systems incorporating text, code, images, and structured data. Agentic AI Systems: Design and operationalize autonomous AI agents with multi-agent orchestration, tool-use capabilities, memory management, and enterprise-grade guardrails and observability strategies. Python & Software Engineering: Write high-performance Python code following SOLID principles, lead code reviews, build reusable AI libraries, and implement rigorous testing and CI/CD practices across all ML workloads Cloud & Distributed Systems: Architect cloud-native AI infrastructure with GPU cluster management, auto-scaling inference endpoints, vector databases, and cost-optimized distributed systems for high-throughput model serving, leveraging managed AI services (such as Bedrock, Azure OpenAI, Vertex AI) alongside self-hosted deployments (such as vLLM, TGI). Backend APIs & Systems Integration: Design production-grade RESTful and asynchronous APIs (similar to FastAPI, gRPC) exposing AI capabilities, integrate LLM services with enterprise systems, and own end-to-end performance, reliability, and security from design through production MLOps & LLMOps: Implement comprehensive ML pipelines for training through monitoring tools (similar to MLflow, Kubeflow, SageMaker ), establish prompt versioning and model governance practices, and instrument production systems with observability across performance and quality metrics DevOps & Platform Engineering: Embed AI workloads into CI/CD pipelines, champion containerization (such as Docker, Kubernetes, Helm) and GitOps workflows, define SRE practices for ML systems, and drive platform standardization for self-service AI capabilities Organization-Wide AI Transformation: Advise engineering, product and business leadership on AI strategy and build-vs-buy decisions, evaluate third-party tooling, define transformation KPIs, and partner with governance teams to establish responsible AI policies and regulatory frameworks. Compensation/Benefits Information: S&P Global states that the anticipated base salary range for this position is $165,000 - $210,000. 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 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. For more information on the benefits we provide to our employees, please click here . Basic Required Qualifications: 10+ years of progressive experience, with 8+ years in data science, data analytics, machine learning engineering, or similar roles. Proven ability to translate complex technical concepts for non-technical audiences with clarity and impact. Experience defining technical roadmaps, architecture decision records (ADRs), and engineering standards adopted across multiple teams. History of mentoring senior and mid-level engineers, conducting effective technical