Senior MLOps Engineer

📋 Description

• Architect and manage scalable GCP-based ML infrastructure using Vertex AI, Google Kubernetes Engine
• Own the end-to-end deployment lifecycle for machine learning models. Build high-throughput
• Build automated, reproducible pipelines for model training, testing, evaluation, and deployment
• Implement robust monitoring systems for both system health (latency, throughput, uptime) and
• Provide scalable training environments, optimized runtime infrastructure, and standardized
• Collaborate with Data Engineers to integrate model pipelines with feature stores, dataset

🎯 Requirements

• At least 5 years of hands-on experience designing, deploying, and maintaining production ML
• Deep, practical experience with Google Cloud Platform (GCP), including Vertex AI, Cloud Storage
• Expertise with containerization (Docker, Kubernetes/GKE) and specialized serving tools (Triton
• Proven track record with workflow orchestrators (Airflow, Vertex AI Pipelines) and modern CI/CD
• Solid experience managing cloud resources using Terraform.
• Proficiency in Python and SQL for scripting, automation, API development, and data manipulation.