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S&P Global

Delivers credit ratings, market data, indices

AI Data Engineer

Data EngineeringPosted on Nov 14
Full-timeNew York, NY, USA$90k - $150k

About the Job

Requirements

  • At least 5 years of hands-on experience in data engineering, including at least 2 years focused on AI/ML workloads.
  • Expert proficiency in Python and experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
  • Strong experience with Databricks, Apache Spark, and distributed computing for machine learning workflows.
  • Deep understanding of the machine learning lifecycle, including model training, deployment, and monitoring processes.
  • Experience with feature engineering, data preprocessing techniques, and machine learning data pipelines.
  • Knowledge of vector databases, embeddings, and similarity search for AI applications.
  • Proficiency in SQL for structured and unstructured data management.
  • Understanding of data governance, model governance, and AI ethics principles.
  • Strong analytical and problem-solving capabilities with attention to data quality.
  • Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field.
  • Candidates must have indefinite right to work within the United States.
  • Candidates must complete a mandatory in-person technical evaluation.

Responsibilities

  • Design and implement specialized data pipelines for AI model metadata, training data lineage, and model performance metrics tracking.
  • Build data infrastructure on Databricks using Spark for large-scale distributed dataset processing.
  • Develop MCP servers and enable AI data distribution via MCP.
  • Develop feature engineering pipelines and data preprocessing workflows for AI model training and inference.
  • Implement model versioning, experiment tracking, and model registry integration using MLflow or similar tools.
  • Create automated workflows for AI agent discovery, classification, and inventory management across the enterprise.
  • Design and maintain knowledge graph structures for representing AI model relationships, dependencies, and data lineage.
  • Build real-time data pipelines for AI model monitoring, drift detection, and performance tracking.
  • Develop data quality frameworks specific to AI training datasets and validation data.
  • Collaborate with data scientists to optimize data access patterns and feature store implementations.
  • Implement security and compliance controls for sensitive AI training data and model artifacts.
  • Create comprehensive documentation for AI data architectures, schemas, and integration patterns.

Desired Qualifications

  • Experience with generative AI applications, including Retrieval-Augmented Generation (RAG) and fine-tuning.
  • Knowledge of LangChain, Hugging Face, or other generative AI frameworks.
  • Familiarity with Azure ML, AWS SageMaker, or Google Vertex AI platforms.
  • Experience with graph databases such as Neo4j or Amazon Neptune for knowledge graph implementation.
  • Understanding of AI model explainability and interpretability techniques.
  • Experience with A/B testing frameworks for machine learning model evaluation.
  • Certification in Databricks, AWS, Azure, or GCP AI/ML services.
  • Publications or contributions to open-source machine learning projects.

About the Company

S&P Global

Delivers credit ratings, market data, indices

S&P Global provides financial information and analytics for investors, corporations, and governments, including credit ratings, market data, and indices. It compiles large financial data, conducts research, and offers analytics, ratings, and benchmarks accessed via subscriptions, licenses, or transaction-based services. The company differentiates itself with an integrated suite that covers ratings, data, analytics, indices, and ESG solutions across global markets, backed by well-known brands. Its goal is to help clients make informed decisions, manage risk, and drive growth while prioritizing corporate responsibility.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1917

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