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Metro location and mid-level experience increase applicants, but specialized MLOps/Azure skills moderate competition.
MLOps skills transfer across industries but Azure-specific and enterprise governance experience make fit moderately sensitive.
Explicit 3+ years MLOps/AIOps and concrete Azure tech stack requirements create strict screening filters.
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Deploy, monitor, and optimize enterprise AI models on Azure, ensuring performance, availability, and cost-efficiency.
Implement MLOps pipelines for continuous integration, deployment, and lifecycle management using Azure ML and GitHub Actions.
Manage compliant change management with auditability, security, and governance in AI deployments across advisory and client-facing applications.
Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field.
Minimum 3 years of experience in MLOps and/or AIOps with hands-on Azure cloud experience including Azure ML, Synapse, Data Lake, App Services, Cosmos DB, and Azure AI Foundry.
Mandatory experience with deploying and operating AI solutions specifically in Azure environment.
Preferred location Mumbai.
Experienced in consulting environments with a strong bias for action and workflow design including prompt flow and automation pipelines.
Skilled in model monitoring, telemetry, incident response, and performance optimization for AI systems.
Proficient with Azure DevOps tools (App Insights, Log Analytics, Key Vault, Managed Identity) and techniques like fine-tuning, RLHF, domain adaptation, and A/B testing frameworks.