AI ML Ops Enterprise Architect

Role: AI ML Ops Enterprise Architect

Descriptions

"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, GCP 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."