Lead Machine Learning Engineer

You will work closely with a world-class AI and ML team comprised of experts in AI and ML modeling, MLOps engineers, data science, and data engineering. Your contributions will be critical in engineering and developing solutions for ML operations, supporting S&Ps AI-driven transformation to drive value both internally and for our customers. This role presents a unique opportunity for ML/MLOps engineers to advance in their career journey.

Role/Responsibilities and Impact:

• ML Engineer to architect, build, and deploy production-grade GenAI services and solutions.
• Work on large-scale stateful and stateless distributed systems, including infrastructure, data ingestion platforms, SQL and NoSQL databases, microservices, orchestration services, and more.
• Lead MLOps/LLMOps platform development & automated pipelines focusing on deploying, monitoring, and maintaining models in production environments; with model governance, cost, and performance optimization.
• Collaborate with cross-functional teams to integrate machine learning models into production systems.
• Create and manage documentation and knowledge base, including development best practices, MLOps/LLMOps processes, and procedures.
• Work closely with members of AI, Data Science, and MLE teams in the development and implementation of S&P Global Ratings Enterprise AI platform.

Basic Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 8+ years of progressive experience as a data analytics, machine learning engineer, or similar roles.
• A minimum of 5 years of experience in data science, data analytics, or related field.
• 5 years of relevant experience with:
  • Writing production-level, scalable code with Python.
  • MLOps/LLMOps, machine learning engineering, or a related role.
  • Search and analytics platforms (such as Elasticsearch, Solr, or OpenSearch), SQL, NoSQL databases, workflow orchestration tools (including but not limited to Apache Airflow, Prefect, or Dagster), distributed data processing frameworks (such as Apache Spark, Apache Flink, or Dask), streaming platforms (like Apache Kafka, Apache Pulsar, or Amazon Kinesis), cloud-based ML platforms (such as Databricks, AWS SageMaker, or Azure ML), and ML lifecycle management tools (like MLflow, Kubeflow, or Weights & Biases).
  • Experience building with LangChain/LangGraph/LangSmith or similar framework technologies.
  • Containerization technologies (such as Docker, Podman, or containerd), container orchestration platforms (including but not limited to Kubernetes, Docker Swarm, or OpenShift), cloud platforms, and CI/CD platforms (such as Jenkins, GitLab CI, or Azure DevOps).
  • Distributed systems programming, AI/ML solutions architecture, and microservices architecture experience.
  • Cloud tools and services (including but not limited to AWS, Azure, or Google Cloud Platform) and SaaS solutions.

Additional Preferred Qualifications:

• 2-3 years of experience with operationalizing data-driven pipelines for large-scale batch and stream processing analytics solutions.
• Experience with contributing to open-source initiatives or in research projects and/or participation in Kaggle competitions, or open-source/GitHub code contributions.
• 6-12 months of experience working with RAG pipelines, prompt engineering, and/or Generative AI use cases.
• Understanding of Agentic AI architecture, including key protocols like MCP, Google A2A.
• LLM/Model API and inference framework experience.