Senior AI Engineer - MLOps/AIOps on Azure

EisnerAmper

Dallas (TX)

On-site

USD 140,000 - 190,000

Full time

2 days ago
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Job summary

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

This role emphasizes secure, observable, and governed machine learning deployments that deliver real business impact, going beyond

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.

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 continuous integration, deployment, and lifecycle management using Azure ML and GitHub Actions.
  • Ensure compliant change management for all AI-related deployments, with auditability, security, and governance controls.

Skills

MLOps
AIOps
Azure cloud
CI/CD workflows

Education

Bachelor's degree in Computer Science or related

Tools

Azure ML
Synapse
Data Lake
App Services
Cosmos DB
Azure AI Foundry
GitHub Actions

Job description

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

This role emphasizes secure, observable, and governed machine learning deployments that deliver real business impact, going beyond

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