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Specialized MLOps role with moderate brand presence and mid-level experience yields medium competition.
Requires specialized GCP MLOps and production ML deployment skills, limiting cross-industry transferability.
Explicit 5-8 years plus mandatory GCP, Vertex AI, and IaC experience makes shortlisting high.
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Design, build, and maintain scalable MLOps platforms on GCP to automate deployment, testing, monitoring, and lifecycle management of Python ML models.
Develop and manage CI/CD pipelines and infrastructure automation (IaC) for batch and real-time ML inference workloads.
Ensure production ML services on Vertex AI and containerized environments are highly available, scalable, secure, and reliable with operational monitoring and governance.
5-8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering.
Strong hands-on experience with Google Cloud Platform (GCP) including Vertex AI, BigQuery, Cloud Storage, Cloud Build, Cloud Run, and Kubernetes Engine.
Demonstrated experience deploying and operationalizing Python-based ML models with CI/CD pipelines and Infrastructure-as-Code (Terraform or similar).
Bachelor's degree in Computer Science, Engineering, IT, or related field.
Experienced in building and managing production-grade MLOps platforms and automated ML workflows using Google Cloud-native services.
Proficient in integrating ML lifecycle processes including model versioning, deployment governance, and operational monitoring on GCP.
Capable of troubleshooting and ensuring reliability, scalability, and secure operations of ML systems in enterprise cloud environments.