Senior MLOps Engineer

The Team: The Data Science/AI/ML team is a newly formed innovation and deployment team within S&P Global Ratings that will be responsible for building and executing a >

The Impact: This role will have a significant impact on the success of our data science, AI, and Machine Learning projects ranging from choosing which projects should be undertaken, to delivering highest quality solution, ultimately enabling our business processes and products with AI and Machine Learning solutions.

We are seeking a senior MLOps Engineer to join our MLOps team within the Data Science COE at S&P Global. As a senior MLOps Engineer, you will contribute to the deployment, monitoring, and management of machine learning models and data pipelines. You will work with a peer group of MlOps engineers to develop MLops modules and engineering solutions.

What's in it for you:

In this role, you will play a pivotal role in implementing our machine learning operations, ensuring the seamless deployment, monitoring, and management of our machine learning models and data pipelines. You will be work closely in a world class AI ML team comprised of experts in AI ML modeling, ML engineers and data science and data engineering teams. You will contribute to engineering and developing solutions for ML operations and be a critical part of leading S&P's AI-driven transformation to drive value internally and for our customers.

S&P is a leader in automation and AI/ML to transform risk management. This role is a unique opportunity for MLops LLMops engineers to grow into the next step in their career journey.

Responsibilities:

ML Model Deployment: Assist in deploying machine learning models into production environments.

AI ML pipeline and model Monitoring: Contribute to the monitoring and maintenance of model performance and infrastructure health.

Automation: Participate in the development and maintenance of automated MLOps pipelines.

Collaboration: Collaborate with cross-functional teams to integrate machine learning models into production systems.

Documentation: Maintain documentation of MLOps processes and procedures.

Work closely with members of technology teams in the development, and implementation of Enterprise AI platform

Problem Solving: Assist in troubleshooting and resolving MLOps-related issues.

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related field.

4+ years experience (1+ years) in cloud engineering MLOps, machine learning engineering, Big Data, or a related role.

Familiarity (ideally 1+ years experience) with containerization, scripting, cloud platforms, and CI/CD.

2+ years experience with Python, Java, Container orchestration systems, Container management platforms, and data and workflow orchestration tools

Familiar with Elasticsearch, SQL, NoSQL, Distributed data processing framework, Big data processing engines, Large-scale data analytics tools, Flink, Databricks and Mlflow,

2+ experience with operationalizing data-driven pipelines for large scale batch and stream processing analytics solutions

Plus: Experience with contributing to github and open source initiatives or in research projects and/or participation in Kaggle competitions