Job Description

: ML Ops Resource (5-7 years experience) We are seeking a highly skilled and experienced ML Ops Resource to join our team. As an ML Ops Resource, you will play a crucial role in deploying and maintaining machine learning models in production environments. Your primary focus will be on automating, optimizing, and streamlining the end-to-end ML lifecycle, from model development to deployment and monitoring. Responsibilities: Collaborate with cross-functional teams including data scientists, engineers, and IT professionals to ensure seamless integration and deployment of machine learning models into production environments. Build and maintain scalable ML infrastructure, including model versioning, deployment pipelines, and monitoring systems, to enable efficient and reliable model deployment. Automate and streamline the ML lifecycle processes, including data preprocessing, feature engineering, model training, validation, and evaluation. Implement and maintain CI/CD (Continuous Integration/Continuous Deployment) pipelines for ML models, ensuring smooth and efficient deployments. Develop and implement model monitoring and alerting systems to detect and address performance issues, data drift, and model degradation. Collaborate with data scientists and engineers to optimize model performance, scalability, and reliability in production environments. Conduct performance profiling and optimization to enhance the efficiency and speed of ML model inference and prediction. Stay up-to-date with the latest ML Ops tools, technologies, and best practices, and propose innovative solutions to improve the ML deployment process. Requirements: Bachelor\'s or Master\'s degree in Computer Science, Data Science, or a related field. Solid experience (5-7 years) in ML Ops, deploying and maintaining machine learning models in production environments. Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow or PyTorch. Strong understanding of AWS cloud platforms and experience with their ML services and infrastructure. Experience with containerization technologies like Docker and container orchestration tools like Kubernetes. Knowledge of CI/CD pipelines, version control systems (e.g., Git), and automation tools (e.g., Jenkins) for ML model deployment. Familiarity with monitoring and logging tools for ML model performance tracking and issue detection. Strong problem-solving and troubleshooting skills, with the ability to diagnose and resolve issues related to ML model deployment and performance. Excellent communication and collaboration skills to work effectively with cross-functional teams and stakeholders. This is an exciting opportunity for an experienced ML Ops professional to contribute to the successful deployment and management of machine learning models in a dynamic and data-driven environment. 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, color, 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: and your request will be forwarded to the appropriate person. US Candidates Only: The EEO is the Law Poster describes discrimination protections under federal law. 20 - Professional (EEO-2 Job Categories-United States of America), IFTECH202.1 - Middle Professional Tier I (EEO Job Group), SWP Priority - Ratings - (Strategic Workforce Planning)

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