Senior AI Engineer: Azure MLOps & AIOps Champion

EisnerAmper

Baton Rouge (LA)

On-site

USD 140,000 - 180,000

Full time

2 days ago
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Job summary

EisnerAmper is seeking a Senior AI Engineer focused on MLOps and AIOps to deploy, operate, and optimize enterprise AI solutions on Azure. You will collaborate with AI Developers, Product Owners, Governance leaders, and Cloud Architects to ensure reliable, scalable, and cost-effective systems across tax, audit, advisory, and client-facing applications.

The role emphasizes secure, observable, and governed machine learning deployments that deliver real business impact, with a culture of innovation

Qualifications

  • 5-7+ years in MLOps and/or AIOps, ideally within Azure cloud environment with hands-on experience with Azure ML, Synapse, Data Lake, App Services, Cosmos DB, and Azure AI Foundry.
  • Bachelor's degree in a related technical field preferred.

Responsibilities

  • Deploy and monitor AI models across Azure services with robust telemetry for performance, drift, and availability.
  • Manage model upgrades including APIs and UIs with structured rollout, version control, and rollback support.
  • Optimize performance and cost through testing, profiling, and tuning of inference infrastructure and pipelines.
  • Implement MLOps pipelines for CI/CD and lifecycle management using Azure ML and GitHub Actions.
  • Ensure compliant change management for all AI deployments with auditability, security, and governance controls.

Skills

MLOps
AIOps
Azure ML
Synapse
Data Lake
App Services
Cosmos DB
Azure AI Foundry

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field

Tools

Azure DevOps
Application Insights
Log Analytics
Key Vault
Managed Identity

Job description

EisnerAmper is seeking a Senior AI Engineer focused on MLOps and AIOps to deploy, operate, and optimize enterprise AI solutions on Azure. You will collaborate with AI Developers, Product Owners, Governance leaders, and Cloud Architects to ensure reliable, scalable, and cost-effective systems across tax, audit, advisory, and client-facing applications.

The role emphasizes secure, observable, and governed machine learning deployments that deliver real business impact, with a culture of innovation

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