Description

Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment. Own the ML Ops strategy, from model development to deployment and monitoring.

Responsibilities

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

Required Skills

• Cloud & Infrastructure: 4+ years designing scalable AWS ML/AI cloud infrastructure for multi-tenant SaaS.
• MLOps Tools: 4+ years experience with AWS SageMaker, GCP Vertex AI, or Databricks.
• Orchestration: 3+ years with ML/Data pipeline orchestration (Kubeflow, Apache Airflow) and DevOps (Argo CD/Argo Workflows).
• Containerization: 3+ years managing Kubernetes and ROSA clusters.
• Data Platforms: 4+ years working with Snowflake, RedShift, or BigQuery.
• Integration: 3+ years in enterprise application integration (Guidewire, Salesforce, Mulesoft).
• Architecture: 3+ years in Data Mesh, Event-Driven Architecture (EDA), and designing scalable, decoupled systems.
• GenAI/LLMs: 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