Description

Architect and implement scalable AWS ML/AI cloud infrastructure for a multi-tenant SaaS environment. Own the ML Ops strategy, model deployment, and monitoring. This role is on-site.

Responsibilities

• Collaborate with data scientists and engineers to define ML workflow requirements and best practices.
• Oversee Kubernetes cluster management and guide decisions on workflow orchestration (Argo vs Kubeflow) and data pipeline governance.
• Integrate ML pipelines with enterprise data and application architectures, including Guidewire and Salesforce.
• Automate infrastructure deployment using Argo CD and mentor teams on ML Ops architecture and security.

Required Skills

• 4+ years designing scalable AWS ML/AI cloud infrastructure for multi-tenant SaaS.
• 4+ years with MLOps tools such as AWS SageMaker, GCP Vertex AI, or Databricks.
• 3+ years with ML/Data pipeline orchestration (Kubeflow, Apache Airflow) and DevOps (Argo CD/Argo Workflows).
• 3+ years managing Kubernetes and ROSA clusters.
• 4+ years working with data platforms like Snowflake, RedShift, or BigQuery.
• 3+ years in enterprise application integration (Guidewire, Salesforce, Mulesoft).
• 3+ years in Data Mesh, Event-Driven Architecture (EDA), and designing scalable, decoupled systems.
• 2+ years with GenAI tools (OpenAI, Gemini) and 1+ year with Agentic AI frameworks (LangGraph, Autogen).

Preferred Skills

• Functional knowledge of Insurance domains (Policy, Claims, Services Ops) and legal/compliance regulations.

Key Skills

Education

Any Gradute