AI/ML Architect (AWS MLOps)

• Architect and implement scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment.
• Collaborate with data scientists, data engineers, and IT teams to define requirements and best practices for ML model development, deployment, and monitoring.
• Evaluate and recommend tools, platforms, and cloud technologies for ML Ops, ensuring alignment with enterprise architecture standards.
• Oversee the integration of ML pipelines with existing enterprise data and application architectures. Familiarity with Guidewire integrations is highly desirable.
• Oversee ML/AI related Kubernetes cluster management and provide guidance on alternative ML/AI workflow orchestration options such as Argo vs Kubeflow, and ML/AI data pipeline creation, management and governance with tools like Airflow.
• Employ tools like Argo CD to automate infrastructure deployment and management.
• Mentor and guide technical teams on ML Ops architecture, tooling, and best practices

Experience Requirements:

• Minimum ten years experience across architecture disciplines with significant enterprise architecture leadership experience required.

Data & Analytics Technology Experience Required

• 5+ years: AI/ML Strategy & Roadmap Development.
• 4+ years: MLOps Tools (Eg. AWS Sagemaker, Google Cloud Platform Vertex AI, Databricks).
• 3+ years: ML & Data Pipeline Orchestration (Eg. Kubeflow, Apache Airflow).
• 2+ years: ML Feature Store Tools (Eg. Tecton, Databricks, FeatureForm).
• 3+ years: DevOps (Eg. Argo CD / Argo Workflows), Containerization (Kubernetes,ROSA).
• 3+ years: Enterprise Application Integration (Eg. Guidewire, Salesforce).
• 4+ years: Data Platforms (Eg. Snowflake, RedShift, BigQuery).
• 2+ years: GenAI Tools / LLMs (Eg. OpenAI, Gemini, etc.).
• 1+ year: Agentic AI Frameworks (Eg. LangGraph, Autogen, Google ADK).
• 3+ years: API Orchestration (Eg. Mulesoft, Google Cloud API).

Architecture Experience Required

• 3+ years: Data Mesh Architecture & Data Product Design.
• 3+ years: Event-Driven Architecture (EDA).
• 4+ years: Scalable AWS ML/AI Cloud Infrastructure (Multi-tenant SaaS).
• 3+ years: Data Architecture Guidelines Development.
• 3+ years: Security in Distributed Systems.
• 4+ years: Designing Scalable, Decoupled Systems.
• 5+ years: Strategy & Roadmap Creation.
• 3+ years: Influencing with Data-Driven Insights.

Domain Experience Required

• 4+ years: Functional Knowledge of Insurance Domains (Policy, Claims, Services Ops) - Preferred.
• 2+ years: Legal & Compliance Regulations in Insurance - Preferred.
• 3+ years: Data Product Development for Functional Domains.
• 2+ years: AI-Driven Business Process Automation."