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Popular DevOps role, metro location, and broad ML/big-data skillset increases competitive density.
Core DevOps skills are transferable, but ML/big-data emphasis increases domain specificity.
Broad mandatory tech stack (Kafka, Flink, Terraform, IaC) implies moderate strictness.
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Design, deploy, and maintain scalable big data and machine learning infrastructure including data pipelines using Apache Flink, Hadoop, and Kafka.
Implement and manage ML model deployment lifecycle ensuring high availability, scalability, and automation through CI/CD pipelines and Infrastructure as Code tools (Terraform, Ansible, CloudFormation).
Monitor, troubleshoot, and optimise production environments, cloud resource utilization (AWS, GCP, Azure), and costs to support data-driven projects.
Experience in managing big data frameworks (Apache Flink, Hadoop, Kafka) and ML Ops infrastructure.
Proficiency with CI/CD pipeline development and Infrastructure as Code using Terraform, Ansible, or CloudFormation.
Strong cloud platform experience with AWS, GCP, or Azure including cost optimisation.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end deployment and scaling of data and ML infrastructure in cloud environments.
Comfortable collaborating cross-functionally with data scientists, data engineers, and software developers for integration.
Focused on automation, reliability, and cost optimisation in production big data and ML systems.