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MLOps Architect

MLOps Architect

Bean HR ConsultingIndia
20 hours ago
Job description

Job Title : Senior Staff Engineer – MLOps

Location : Bangalore, India

Work Type : Hybrid (part remote, part onsite)

Experience : 11–15 years

Employment Type : Full-time

Role Overview

We 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 Responsibilities

  • Build 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 Skills

  • Programming : Python (mandatory), SQL
  • MLOps / 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 Hubs
  • CI / CD : Designing pipelines for ML workflows
  • Infrastructure as Code (IaC) : Terraform, ARM Templates, Bicep
  • Data Handling : Azure Data Lake, Blob Storage, Synapse Analytics
  • Monitoring & Logging : Azure Monitor, Prometheus, Grafana, Application Insights
  • ML Lifecycle : Data preprocessing, model training, deployment, monitoring
  • Preferred Skills

  • Deploying ML models on Azure Kubernetes Service (AKS)
  • Knowledge of feature stores and distributed training frameworks
  • Familiarity with RAG (Retrieval-Augmented Generation) pipelines and LLMOps
  • Relevant Azure certifications (AI Engineer, Data Scientist, DevOps Engineer)
  • Other Details

  • Travel : Up to 10%
  • Time Type : Full-time
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