Description

Design and implement scalable ML/AI cloud infrastructure on AWS within a multi-tenant SaaS environment. Own the architecture, deployment, and monitoring of ML systems.

This role is on-site.

Responsibilities

• Architect and manage ML pipelines and data workflows using Databricks, Kubeflow, and Apache Airflow.
• Oversee Kubernetes-based ML/AI environments and guide orchestration using Argo or Kubeflow.
• Implement CI/CD and infrastructure automation using Argo CD.
• Define best practices for ML model development, deployment, and monitoring in collaboration with data scientists and engineers.
• Ensure scalability, reliability, and governance of ML data pipelines and AI systems.

Required Skills

• 10+ years of experience in Machine Learning, Data Engineering, or Cloud Architecture.
• Strong hands-on experience with AWS cloud infrastructure and ML workloads.
• Expertise with MLOps platforms such as Databricks, AWS SageMaker, or GCP Vertex AI.
• Proficiency with ML pipeline orchestration tools including Kubeflow or Apache Airflow.
• Strong experience with containerization and Kubernetes-based deployments.
• Experience with DevOps tools such as Argo CD or Argo Workflows.
• Knowledge of enterprise data platforms such as Snowflake, Redshift, or BigQuery.
• Strong knowledge of API orchestration and enterprise integrations.

Key Skills

Education

Any Graduate