Description

You will architect and implement scalable AWS ML/AI cloud infrastructure within a multi-tenant SaaS environment, owning the end-to-end ML lifecycle and platform reliability.

This role is on-site.

Responsibilities

• Design and deploy ML pipelines, overseeing orchestration using Airflow or Kubeflow.
• Manage ML/AI Kubernetes cluster operations and guide teams on workflow orchestration choices.
• Automate infrastructure deployment and management using tools like Argo CD.
• Collaborate with data scientists and engineers to define ML model lifecycle requirements.
• Mentor technical teams on MLOps architecture and tooling standards.

Required Skills

• 4+ years experience with MLOps tools (AWS SageMaker, GCP Vertex AI, Databricks).
• 3+ years in DevOps practices, including containerization with Kubernetes and ROSA.
• 3+ years experience with ML and Data Pipeline Orchestration (Kubeflow, Apache Airflow).
• 4+ years designing and working with scalable AWS ML/AI Cloud Infrastructure in multi-tenant SaaS.
• 4+ years experience with Data Platforms (Snowflake, Redshift, BigQuery).
• 3+ years experience with Enterprise Application Integration (Guidewire, Salesforce).
• 3+ years experience with API Orchestration (Mulesoft).
• 2+ years experience with GenAI Tools / LLMs (OpenAI, Gemini, etc.).
• 3+ years experience in designing scalable, decoupled systems.

Key Skills

Education

Any Gradute