zoho_recruit
MLOps Engineer
Rubiscape
Pune, IND
Full-time
Mid · 3+ years experience
Onsite
Discovered 3 weeks ago
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Free
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Role Overview
Rubiscape’s RubiStudio studio promises enterprises a path from experiment to production in under 90 days — and the MLOps Engineer is the person who makes that promise real. You will design the CI/CD pipelines, model registries, deployment orchestration, and monitoring infrastructure that keep hundreds of ML models running reliably across SaaS, BYOC, on-premises, and air-gap deployments. You will work closely with ML Engineers, Platform Engineers, and enterprise customer success teams to eliminate the gap between model training and business value.
Key Skills for This Role
MLflowKubeflowAirflowPrefectFeastTecton
Full Job Posting
About the Role
Rubiscape’s RubiStudio studio promises enterprises a path from experiment to production in under 90 days — and the MLOps Engineer is the person who makes that promise real. You will design the CI/CD pipelines, model registries, deployment orchestration, and monitoring infrastructure that keep hundreds of ML models running reliably across SaaS, BYOC, on-premises, and air-gap deployments. You will work closely with ML Engineers, Platform Engineers, and enterprise customer success teams to eliminate the gap between model training and business value.
Key Responsibilities
- · Build and maintain end-to-end ML pipelines using MLflow, Kubeflow, or Airflow that handle training, validation, packaging, and deployment of models at scale.
- · Design the model registry architecture within RubiStudio: versioning strategies, stage transitions (staging → canary → production), approval gates, and rollback mechanisms.
- · Implement automated model monitoring for data drift, concept drift, and prediction quality degradation, surfacing alerts into RubiSight operational dashboards.
- · Manage containerised model serving infrastructure (Docker + Kubernetes) across multi-cloud and on-premises deployment topologies aligned with Rubiscape’s deployment flexibility.
- · Define and enforce MLOps best practices: reproducible experiments, environment parity, feature store integration, and audit-ready lineage for regulated-sector customers.
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