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 that have high throughput availability, consistency, and low latency. The Predictive Analytics AI Group is the central group in Moody’s Analytics comprising of researchers and engineers working together to build data-driven customer-facing products, as well as the necessary infrastructure to support the ML services following the industry leading practices. The group has worked on and built some award-winning AI products like Compliance Catalyst , Adverse Media Monitoring, Coronapulse , Quiqspread , News Edge 2.0 , ESG and has participated in various internal automation initiatives. The group also regularly publish and present their work in top-tier academic and industry conferences. We have a flexible work environment and allow remote work depending on one’s personal choice. Responsibilities: As the Machine Learning Ops Engineer for the AI Team you will: Work closely with the Data Science team and the Data Engineers and DevOps teams in order to deploy machine learning models. Specifically execute continuous integration and continuous delivery (CI/CD) activities to release ML code and ML pipelines into a Production environment Maintain the Machine Learning pipeline and make sure everything is running 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, monitoring capabilities and facilitates resolution of issues with the appropriate teams SKILLS AND EXPERIENCE Must Have: Proven industry/commercial/research lab experience (2+ years) deploying machine learning models and maintaining ML pipelines, orchestration, deployment, monitoring, & 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 & SQL Experience in containerization and orchestration (such as Docker, Kubernetes) Practical Knowledge of Machine Learning models in commercial settings Good communication skills Nice to Have: Experience building batch and/or real-time data & 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)