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