AI Data Engineer Full Time

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