Data Engineer

Role & responsibilities Deploy and monitor production AI models across Azure services ensuring system availability and reliability. Implement robust telemetry frameworks to track model performance, data drift, and inference metrics. Build and maintain automated MLOps pipelines for continuous integration and lifecycle management using Azure ML and GitHub Actions. Manage end-to-end model upgrades including APIs and UIs with structured rollout, version control, and rollback mechanisms. Optimize cloud infrastructure performance and compute costs through rigorous profiling, testing, and tuning of inference pipelines. Ensure compliant change management practices across all deployments to meet enterprise security and auditability standards. Collaborate with cross-functional teams including AI Developers, Cloud Architects, and Governance leaders to scale operations Preferred candidate profile Experience: 4+ years of professional experience in MLOps and/or AIOps roles. Consulting background is highly preferred. Education: Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field. Technical Expertise: Strong proficiency in Azure cloud tools including Azure ML, Synapse, Data Lake, Cosmos DB, and Azure AI Foundry. Automation & Testing: Hands-on experience with workflow design (Prompt flow), Azure DevOps, and inference performance testing tools like Locust or K6. Advanced ML Concepts (Assumed): Familiarity with post-training techniques like fine-tuning, instruction tuning, model evaluation metrics, and A/B testing frameworks.