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Tier-1 brand plus hybrid remote and specialized ML platform requirements create moderate competition.
Deep AI platform, cloud and MLOps requirements make skills less transferable across unrelated industries.
Explicit 7+ years and mandatory cloud, MLOps, and GenAI skills enforce strict candidate filtering.
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Lead design and deployment of scalable, secure AI/ML infrastructure across Azure, AWS, and Databricks using Infrastructure-as-Code and CI/CD pipelines.
Build and maintain cloud-native platforms to support generative AI and Agentic AI workloads with embedded security, compliance, monitoring, and cost management.
Collaborate with data scientists, security, and application teams to integrate AI services and provide mentorship to junior engineers.
7+ years experience in platform engineering with proven expertise in Azure and AWS cloud services (e.g., Azure AI, Azure Kubernetes, AWS SageMaker, AWS Bedrock).
Proficiency with Infrastructure-as-Code tools like Terraform, Azure ARM/Bicep, AWS CloudFormation, and DevOps CI/CD pipelines (Azure DevOps, AWS CodePipeline).
Strong understanding of cloud security, governance, compliance (e.g., IAM, Azure Policies, AWS SCPs), and monitoring tools (Grafana, Prometheus, Azure Monitor).
Bachelor's or master's degree in computer science, engineering, or related numerate field.
Experienced in architecting and automating AI/ML platforms supporting generative AI technologies and managing model lifecycle (MLOps).
Deep knowledge of multi-cloud environments with hands-on skills in advanced networking, cloud security, and integration of AI services across enterprises.
Capable of leading technical teams, troubleshooting complex AI platform issues, and driving cloud cost optimization initiatives.