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# AI Engineer

- **Company:** Eisner Advisory Group LLC
- **Location:** Woodbridge Township, NJ, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Microsoft Azure, Continuous Integration, Github, Log Analysis, Performance Tuning, Azure Machine Learning, Workflow Management Systems, Scripting, Performance Testing, Delivery Pipeline, Model Validation, Data Lakes, Information Technology, Cosmos DB, Machine Learning Operations, Azure Synapse Analytics, Software Version Control, Key Vault
- **Published:** August 2, 2026
- **Apply:** https://eisneramper.wd1.myworkdayjobs.com/EisnerAmper_External/job/Iselin/AI-Engineer_Req-9227

## About the Role

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

Preferred/Desired Qualifications

 * Consulting background with a strong bias for action.
 * Workflow Design: Prompt flow, automation pipelines, and human-in-the-loop systems
 * Post-Training Techniques: Fine-tuning, instruction tuning, RLHF, and domain adaptation
 * Azure DevOps, App Insights, Log Analytics, Key Vault, and Managed Identity integration.
 * Tools for inference performance testing and profiling (e.g., locust, K6, or custom scripts).
 * Model Evaluation: Performance metrics, benchmark development, and A/B testing frameworks
 * Understanding of model observability, telemetry, and incident response for AI systems.

## Description

* 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.


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