S&P Global
GenAI ML/MLOps Engineering Lead (Remote or Hybrid)
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GenAI ML/MLOps Engineering Lead (Remote or Hybrid)
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S&P Global About The Role:
We are seeking a Lead/Associate Director of ML & MLOps Engineering - GenAI to join our ML team within the Data Science COE at S&P Global, focusing on building Generative AI solutions. You will lead the engineering activities for building production-grade generative AI solutions, ensuring seamless deployment, monitoring, and management of models and data pipelines. The Team:
You will work closely with a world-class AI ML team, including experts in AI ML modeling, ML & LLMOps engineers, data science, and data engineering. You will contribute to ML operations solutions and be a key part of S&P’s AI-driven transformation to deliver value internally and to customers. Responsibilities and Impact:
Lead ML engineering efforts to architect, build, and deploy production-grade GenAI services and solutions. Work on large-scale distributed systems, including infrastructure, data ingestion platforms, databases, microservices, and orchestration services. Develop MLOps/LLMOps platforms and automated pipelines for deploying, monitoring, and maintaining models, with governance, cost, and performance optimization. Collaborate with cross-functional teams to integrate machine learning models into production systems. Create and maintain documentation, best practices, and processes for MLOps/LLMOps. Work with technology teams on developing and implementing the Enterprise AI platform. Qualifications:
Minimum Requirements: Bachelor's degree in Computer Science, Engineering, or related field. 8+ years of experience in machine learning, data analytics, or similar roles. 5+ years of experience with: Python (or Scala) coding at production level. MLOps/LLMOps, machine learning engineering, Big Data, etc. Tools like Elasticsearch, SQL, NoSQL, Apache Airflow, Spark, Kafka, Databricks, MLflow. Containerization, Kubernetes, cloud platforms, CI/CD, workflow orchestration. Distributed systems, AI/ML solutions architecture, Microservices. Preferred Qualifications: 2-3 years of operationalizing large-scale data pipelines. Open-source contributions, research projects, Kaggle experience. Experience with RAG pipelines, prompt engineering, Generative AI use cases. Experience with SageMaker and/or Vertex AI.
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Join to apply for the
GenAI ML/MLOps Engineering Lead (Remote or Hybrid)
role at
S&P Global About The Role:
We are seeking a Lead/Associate Director of ML & MLOps Engineering - GenAI to join our ML team within the Data Science COE at S&P Global, focusing on building Generative AI solutions. You will lead the engineering activities for building production-grade generative AI solutions, ensuring seamless deployment, monitoring, and management of models and data pipelines. The Team:
You will work closely with a world-class AI ML team, including experts in AI ML modeling, ML & LLMOps engineers, data science, and data engineering. You will contribute to ML operations solutions and be a key part of S&P’s AI-driven transformation to deliver value internally and to customers. Responsibilities and Impact:
Lead ML engineering efforts to architect, build, and deploy production-grade GenAI services and solutions. Work on large-scale distributed systems, including infrastructure, data ingestion platforms, databases, microservices, and orchestration services. Develop MLOps/LLMOps platforms and automated pipelines for deploying, monitoring, and maintaining models, with governance, cost, and performance optimization. Collaborate with cross-functional teams to integrate machine learning models into production systems. Create and maintain documentation, best practices, and processes for MLOps/LLMOps. Work with technology teams on developing and implementing the Enterprise AI platform. Qualifications:
Minimum Requirements: Bachelor's degree in Computer Science, Engineering, or related field. 8+ years of experience in machine learning, data analytics, or similar roles. 5+ years of experience with: Python (or Scala) coding at production level. MLOps/LLMOps, machine learning engineering, Big Data, etc. Tools like Elasticsearch, SQL, NoSQL, Apache Airflow, Spark, Kafka, Databricks, MLflow. Containerization, Kubernetes, cloud platforms, CI/CD, workflow orchestration. Distributed systems, AI/ML solutions architecture, Microservices. Preferred Qualifications: 2-3 years of operationalizing large-scale data pipelines. Open-source contributions, research projects, Kaggle experience. Experience with RAG pipelines, prompt engineering, Generative AI use cases. Experience with SageMaker and/or Vertex AI.
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