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Mid-level metro AI/MLOps role with commonly sought skills increases applicant competition.
Requires specific Azure ML, MLOps, and observability experience, reducing cross-industry transferability.
Explicit 3-5 year requirement plus mandatory Azure, MLOps, and observability tech creates high shortlisting strictness.
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Design, develop, and operationalize AI/ML solutions on Microsoft Azure using Azure AI Services, Data Services, and integrate into platform operations.
Embed AI capabilities into CI/CD pipelines, automation workflows, and observability tools to improve operational efficiency, reliability, and predictive monitoring.
Implement MLOps for deployment and monitoring of AI models while ensuring compliance with security and governance standards like SOX.
3-5 years of experience in AI/ML, Site Reliability Engineering (SRE), or Observability engineering with production-grade AI/ML deployments.
Hands-on experience with Microsoft Azure cloud-native applications, Azure AI Services, Azure Data Services, and scripting (PowerShell, Python, Bash).
Experience with CI/CD tools such as GitHub Actions or Azure DevOps and infrastructure as code tools like Terraform or Bicep.
Work Experience Required: 3-5 years in relevant domains
Experienced in embedding AI/ML within platform engineering and automation pipelines, particularly on Azure ecosystems.
Familiar with operationalizing AI models through MLOps with a focus on reliability, cost optimization, and compliance.
Capable of integrating AI into observability and monitoring tools (Grafana, Azure Monitor) to enable predictive maintenance and intelligent alerting.