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 qualified 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.

📌 Data Engineer (Mumbai)
🏢 SG Analytics
📍 Mumbai