The Role :

As an AI Data Engineer specializing in MLOps and AIOps, you will play a critical role in deploying, operating, and optimizing enterprise-grade AI solutions built on Azure. You will collaborate closely with AI Developers, Product Owners, Governance leaders, and Cloud Architects to ensure these systems are reliable, scalable, and cost-effective.

This role goes beyond traditional DevOps- "it's about engineering AI into the fabric of enterprise operations, enabling secure, observable, and governed machine learning deployments that deliver measurable business value.

What You Will Be Responsible For :

- 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

What We're Looking For :

Basic Qualifications :

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

- 3+ years of hands-on experience in MLOps and/or AIOps, ideally within an Azure cloud environment

- Demonstrated expertise 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

- Experience with Workflow Design : Prompt flow, automation pipelines, and human-in-the-loop systems

- Knowledge of Post-Training Techniques : Fine-tuning, instruction tuning, RLHF, and domain adaptation

- Proficiency with Azure DevOps, App Insights, Log Analytics, Key Vault, and Managed Identity integration

- Experience with tools for inference performance testing and profiling (e.g., locust, K6, or custom scripts)

- Strong understanding of Model Evaluation : Performance metrics, benchmark development, and A/B testing frameworks

- Knowledge of model observability, telemetry, and incident response for AI systems