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Mid-level ML/AI role, metro locations, and broad Azure/LLM skillset create high candidate competition.
Requires specialized Azure AI, LLM, and MLOps experience, making cross-industry transfers difficult.
Explicit 3-5 years plus mandatory Azure AI, MLOps, and compliance requirements make shortlisting highly selective.
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Design, develop, and deploy AI/ML solutions using Azure AI Services, integrating these into platform engineering and automation pipelines to enhance operational efficiency and reliability.
Automate operational tasks such as incident analysis, monitoring insights, and ticketing by embedding AI capabilities into CI/CD pipelines and deployment frameworks.
Implement scalable AI data pipelines, predictive monitoring, anomaly detection, and apply MLOps practices ensuring compliance with security and governance standards (e.g., SOX).
3-5 years of experience in AI/ML, SRE, or Observability engineering with production-grade AI/ML deployments.
Hands-on experience with Microsoft Azure cloud-native applications and Azure AI Services including Azure OpenAI, Cognitive Services, ML Studio.
Proficiency in scripting languages (PowerShell, Python, Bash), CI/CD tools (GitHub Actions, Azure DevOps), and automation frameworks.
Experience with AI Agents, Copilot integration, LLMs, Prompt Engineering, RAG architectures, Infrastructure as Code (Terraform, Bicep).
Experienced in embedding AI into platform engineering and observability to drive operational intelligence and automation at scale.
Familiar with implementing MLOps and enterprise security/compliance standards, capable of delivering robust AI-driven operational solutions.
Comfortable operating in cloud-native environments with strong skills in Azure data services and automation tooling to optimize AI costs and processes.