MLOps Architect

1 day ago

Bengaluru, Karnataka, India Bean HR Consulting Full time ₹ 12,00,000 - ₹ 24,00,000 per year

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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