Principal Machine Learning Engineer Job at S&P Global, Inc. in New York
S&P Global, Inc., New York, NY, United States
About the Role
Grade Level (for internal use): 12
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 mentoring 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 operationalisation 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 operationalise 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‑optimised 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 containerisation (such as Docker, Kubernetes, Helm) and GitOps workflows, define SRE practices for ML systems, and drive platform standardisation for self‑service AI capabilities.
Organisation‑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, licences 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, contact the benefits office.
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 interviews, and raising the organisational engineering bar.
LLM Frameworks: Extensive knowledge and experience in tools similar to LangChain, LlamaIndex, LangGraph, Hugging Face Transformers, PEFT, vLLM, Ollama, or equivalent production‑grade tooling.
MLOps Tooling: Extensive knowledge and experience with tools similar to MLflow, SageMaker, Vertex AI, or Kubeflow — with a bias toward automation and repeatability.
Cloud Platforms: Deep expertise in cloud platforms such as AWS, GCP, or Azure.
Python: Expert‑level proficiency including async programming, performance optimisation, type systems, packaging, and internal library authorship.
Databases & Storage: Vector databases similar to Pinecone, OpenSearch, Chroma; relational such as PostgreSQL; NoSQL such as Redis, DynamoDB; and object storage.
Containerisation & Orchestration: Expertise in Docker, Kubernetes, Helm.
Backend Development: Expertise in FastAPI, REST design principles, async patterns, OAuth2/JWT, and API security best practices.
Distributed Systems: Experience with message queues such as Kafka, SQS, event streaming, microservices design patterns.
Preferred Qualifications
MS in Computer Science, Machine Learning, Engineering, or a related quantitative field.
Published open‑source contributions in the LLM, GenAI or NLP space.
Experience operating in regulated industries (finance, healthcare, legal) with AI compliance, auditability, and risk management requirements.
Contributions to enterprise AI governance frameworks, model risk management programmes, or responsible AI practices development.
Cloud AI certifications: AWS ML Specialty, GCP Professional ML Engineer, Azure AI Engineer Associate, or equivalent.
Benefits
Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized.
Continuous Learning: Access resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company‑matched student loan contribution, and financial wellness programmes.
Family Friendly Perks: Perks for your partners and little ones, with best‑in‑class benefits for families.
Beyond the Basics: Retail discounts, referral incentive awards, and other small perks that make a big difference.
Equal Opportunity Employer
S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race, ethnicity, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status or any other status protected by law. Only electronic job submissions will be considered for employment.
If you need an accommodation during the application process due to a disability, please send an email to EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.
US Candidates Only
Know Your Rights: Workplace discrimination is illegal.
Job ID: 312048
Posted On: 2026-03-24
Location: New York, New York, United States
#J-18808-Ljbffr
Grade Level (for internal use): 12
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 mentoring 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 operationalisation 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 operationalise 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‑optimised 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 containerisation (such as Docker, Kubernetes, Helm) and GitOps workflows, define SRE practices for ML systems, and drive platform standardisation for self‑service AI capabilities.
Organisation‑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, licences 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, contact the benefits office.
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 interviews, and raising the organisational engineering bar.
LLM Frameworks: Extensive knowledge and experience in tools similar to LangChain, LlamaIndex, LangGraph, Hugging Face Transformers, PEFT, vLLM, Ollama, or equivalent production‑grade tooling.
MLOps Tooling: Extensive knowledge and experience with tools similar to MLflow, SageMaker, Vertex AI, or Kubeflow — with a bias toward automation and repeatability.
Cloud Platforms: Deep expertise in cloud platforms such as AWS, GCP, or Azure.
Python: Expert‑level proficiency including async programming, performance optimisation, type systems, packaging, and internal library authorship.
Databases & Storage: Vector databases similar to Pinecone, OpenSearch, Chroma; relational such as PostgreSQL; NoSQL such as Redis, DynamoDB; and object storage.
Containerisation & Orchestration: Expertise in Docker, Kubernetes, Helm.
Backend Development: Expertise in FastAPI, REST design principles, async patterns, OAuth2/JWT, and API security best practices.
Distributed Systems: Experience with message queues such as Kafka, SQS, event streaming, microservices design patterns.
Preferred Qualifications
MS in Computer Science, Machine Learning, Engineering, or a related quantitative field.
Published open‑source contributions in the LLM, GenAI or NLP space.
Experience operating in regulated industries (finance, healthcare, legal) with AI compliance, auditability, and risk management requirements.
Contributions to enterprise AI governance frameworks, model risk management programmes, or responsible AI practices development.
Cloud AI certifications: AWS ML Specialty, GCP Professional ML Engineer, Azure AI Engineer Associate, or equivalent.
Benefits
Health & Wellness: Health care coverage designed for the mind and body.
Flexible Downtime: Generous time off helps keep you energized.
Continuous Learning: Access resources to grow your career and learn valuable new skills.
Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company‑matched student loan contribution, and financial wellness programmes.
Family Friendly Perks: Perks for your partners and little ones, with best‑in‑class benefits for families.
Beyond the Basics: Retail discounts, referral incentive awards, and other small perks that make a big difference.
Equal Opportunity Employer
S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race, ethnicity, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status or any other status protected by law. Only electronic job submissions will be considered for employment.
If you need an accommodation during the application process due to a disability, please send an email to EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person.
US Candidates Only
Know Your Rights: Workplace discrimination is illegal.
Job ID: 312048
Posted On: 2026-03-24
Location: New York, New York, United States
#J-18808-Ljbffr