Description

You will manage and scale machine learning workflows and real-time model serving infrastructures across AWS, GCP, and on-premises clusters.

Responsibilities

• Deploy and configure large-scale applications within production Kubernetes clusters.
• Develop and maintain CI/CD pipelines using frameworks like ArgoCD.
• Implement real-time system statistics collection and performance monitoring methods.
• Collaborate with data scientists to support feature engineering and ML workflows.
• Manage ML infrastructure across AWS, GCP, and on-premises environments.

Required Skills

• 4+ years of experience in computer software development.
• 2+ years of experience in AI or Client Engineering.
• Hands-on experience with Kubernetes architecture, deployment, and troubleshooting.
• Proficiency in Python and Java.
• Experience with MLOps and cloud infrastructure in AWS and GCP.
• Strong understanding of Data Science, AIML practices, and RTML model serving.
• Familiarity with CI/CD processes and ArgoCD.
• Knowledge of real-time system stats collection and performance monitoring.
• Ability to communicate technical concepts to non-technical business stakeholders.

Preferred Skills

• Experience with RT Feature Engineering methodologies.

Key Skills

Education

Any Graduate