MLOps Engineer

Role Summary We are seeking a highly skilled MLOps Engineer to focus on the deployment, monitoring, and maintenance of machine learning models in production environments. This role is platform-focused and does not involve model development or end-user support. The successful candidate will ensure reliability, scalability, and performance of ML platforms while managing API endpoints and deployment workflows. Key Responsibilities Platform Operations & Monitoring • Monitor ML model endpoints and platform health using tools such as Grafana and Domino Data Lab • Respond to incidents and alerts; perform code fixes and manage changes via ServiceNow • Liaise with Domino Data Lab support to resolve platform-related issues Model Deployment • Deploy and maintain ML models in production environments • Ensure models integrate seamlessly into automated pipelines • Maintain reliability, version control, and governance standards Pipeline Maintenance • Collaborate with Data Scientists and Engineers for smooth production handoff • Maintain and optimize ML pipelines for stability and scalability • Improve performance, resource usage, and automation Automation & Tooling • Implement automation for deployment and monitoring • Contribute to continuous platform improvements Required Skills & Experience • Strong Python programming experience • Proven experience deploying and monitoring ML models in production • Understanding of model evaluation metrics, data drift, overfitting, and feature importance • Experience with AWS services (S3, Redshift, etc.) • Hands-on experience with Grafana for monitoring • Familiarity with Domino Data Lab (desirable) • Strong knowledge of CI/CD, version control, Docker, Kubernetes • Excellent troubleshooting and incident management skills • Strong stakeholder communication skills