1. Monitoring:

Continuously track Domino platform uptime, resource utilization, and health

Monitor availability, latency, and throughput of real-time and batch endpoints.

Maintain dashboards (Grafana) for platform and deployment metrics. Ensure proper incident logging for audit and troubleshooting

Monitor resource utilization (CPU, GPU, memory, network traffic), computational performance, and model aging.

Keep tabs on changes to dependencies, such as data version or software upgrades.

2. Operations: 

Use ServiceNow for Incident logging. Act as first responder for platform-related incidents. Triage, root cause analysis, and resolution for outages or performance issues. Document RCA and drive preventive measures.

Coordinate with Domino Data Lab for platform support.

Deploy and Maintain ML models in production environments.

Domino User onboarding as per SOPs

3. MLOps Related Development

Design, build and maintain automated ML. pipelines to incorporate CI/CD workflows and rapid deployment of models (Dev Staging → Prod) as well as continuous training (CT) and experimentation.

Build and maintain shared tools/utilities to accelerate model development.

Build automation for audit trails, model lineage, and approval workflows. Integrate model monitoring into deployment workflows.

Collaborate with data scientists and engineers to ensure smooth handoff from model development to production.