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