MLOps Engineer

In the Predictive Analytics AI group, we build data‑driven, highly distributed machine learning systems. Our engineers and researchers are responsible for architecting and developing these ML services end‑to‑end, overcoming unique challenges that involve building systems with high throughput, availability, consistency, and low latency. The Predictive Analytics AI Group is the central group in Moody’s Analytics comprising researchers and engineers working together to build data‑driven customer‑facing products and the necessary infrastructure to support the ML services following industry‑leading practices.

The group has worked on and built award‑winning AI products such as Compliance Catalyst, Adverse Media Monitoring, Corona pulse, Quiqspread, News Edge 2.0, ESG, and has participated in various internal automation initiatives. The group also regularly publishes and presents its work at top‑tier academic and industry conferences. We have a flexible work environment and allow remote work depending on one’s personal choice.

• Work closely with the Data Science team, Data Engineers, and Dev Ops teams to deploy machine learning models, executing continuous integration and continuous delivery (CI/CD) activities to release ML code and pipelines into production.
• Maintain the machine learning pipeline, ensuring everything runs accurately and reliably.
• Liaise with senior stakeholders across the data function and the wider business.
• Use industry best practices such as code reviews, pull requests, and peer testing to ensure high quality AI/ML deliverables.
• Build AI/ML model performance benchmarking, evaluation, and monitoring capabilities and facilitate resolution of issues with the appropriate teams.

• Proven industry, commercial, or research lab experience (2+ years) deploying machine learning models and maintaining ML pipelines, orchestration, deployment, monitoring, and support.
• Experience creating and maintaining deployment pipelines with CI/CD tools (2+ years).
• Knowledge of cloud technologies (e.g., AWS) and extensive programming experience in Python and SQL.
• Experience in containerization and orchestration (such as Docker, Kubernetes).
• Practical knowledge of machine learning models in commercial settings.
• Good communication skills.

• Experience building batch and/or real‑time data and ML pipelines.
• Familiarity with MLflow (or similar platforms like Kubeflow and other tools).
• Promotes a practice of unifying system development (Dev) and system operations (Ops).