MLOps Architect

3 days ago


Bengaluru, India Bean HR Consulting Full time

Job Title: Senior Staff Engineer – MLOpsLocation: Bangalore, IndiaWork Type: Hybrid (part remote, part onsite)Experience: 11–15 yearsEmployment Type: Full-timeRole OverviewWe are looking for a Senior Staff Engineer – MLOps to design, build, and maintain end-to-end ML pipelines. You will work with Data Scientists, Data Engineers, and DevOps teams to productionize ML models and ensure they run efficiently, securely, and reliably in Azure cloud environments.Key ResponsibilitiesBuild and maintain ML pipelines for data preprocessing, model training, testing, and deployment.Collaborate with Data Scientists to productionize ML models in Azure ML and Databricks.Implement CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.Automate infrastructure provisioning with Terraform, ARM Templates, or Bicep.Deploy and monitor ML models using Azure Monitor, Application Insights, Prometheus, Grafana, MLflow.Implement model versioning, experiment tracking, and artifact management.Ensure security, compliance, and cost optimization for deployed ML solutions.Develop alerts and monitoring for model drift, data drift, and performance degradation.Work cross-functionally with Data Engineers, DevOps Engineers, and Data Scientists to streamline ML delivery.Required SkillsProgramming: Python (mandatory), SQLMLOps / DevOps Tools: MLflow, Azure DevOps, GitHub Actions, Docker, Kubernetes (AKS)Azure Services: Azure ML, Azure Databricks, Azure Data Factory, Azure Storage, Azure Functions, Azure Event HubsCI/CD: Designing pipelines for ML workflowsInfrastructure as Code (IaC): Terraform, ARM Templates, BicepData Handling: Azure Data Lake, Blob Storage, Synapse AnalyticsMonitoring & Logging: Azure Monitor, Prometheus, Grafana, Application InsightsML Lifecycle: Data preprocessing, model training, deployment, monitoringPreferred SkillsDeploying ML models on Azure Kubernetes Service (AKS)Knowledge of feature stores and distributed training frameworksFamiliarity with RAG (Retrieval-Augmented Generation) pipelines and LLMOpsRelevant Azure certifications (AI Engineer, Data Scientist, DevOps Engineer)Other DetailsTravel: Up to 10%Time Type: Full-time



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