AIML 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 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 (KubernetesROSA).
• 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.