
MLOps Architect
4 days ago
Key Responsibilities:
Build and maintain ML pipelines for training, validation, deployment, and monitoring Implement CI/CD for ML artifacts, including data versioning and model registries
Monitor model performance and drift; trigger retraining workflows as needed Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycle
Required Skills:
Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI) Proficiency in Python, Bash, Docker, and Kubernetes
Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation) Experience in model monitoring, logging, and alerting
Bonus: experience in BPS / regulated domains with compliance-aware deployment"
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MLOps Architect
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MLOPs Engg
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