Description

Experience Required: 10+ years

Job Summary:
We are seeking a Senior AI/ML Ops Enterprise Architect with 10+ years of enterprise architecture experience and strong expertise in AWS cloud, MLOps, AI/ML infrastructure, Kubernetes, data architecture, and enterprise integrations.

The ideal candidate will architect scalable ML/AI platforms in a multi-tenant SaaS environment, establish MLOps strategy and best practices, and work closely with data scientists, data engineers, DevOps, and enterprise IT teams.

Key Responsibilities

• Architect and implement scalable AWS ML/AI cloud infrastructure for multi-tenant SaaS environments.
• Define enterprise standards and best practices for ML model development, deployment, monitoring, and governance.
• Collaborate with data scientists, data engineers, and IT teams to establish MLOps requirements and architecture.
• Evaluate and recommend MLOps platforms and technologies such as AWS SageMaker, Databricks, and Vertex AI.
• Integrate ML pipelines with enterprise data and application architectures; Guidewire integration experience is highly desirable.
• Provide architecture guidance for Kubernetes-based ML/AI environments.
• Evaluate and guide workflow orchestration technologies such as Argo, Kubeflow, and Apache Airflow.
• Design and govern ML/AI data pipelines, feature stores, and data products.
• Use Argo CD / Argo Workflows for automated infrastructure and application deployment.
• Establish security, governance, scalability, and reliability standards for distributed AI/ML systems.
• Mentor technical teams on MLOps architecture, tooling, DevOps practices, and enterprise architecture standards.

Required Qualifications

• 10+ years of experience across architecture disciplines with significant enterprise architecture leadership.
• 5+ years developing AI/ML strategy and technology roadmaps.
• 4+ years of MLOps experience with platforms such as AWS SageMaker, GCP Vertex AI, or Databricks.
• 3+ years with ML/data pipeline orchestration technologies such as Kubeflow and Apache Airflow.
• 2+ years with ML feature store technologies such as Tecton, Databricks, or FeatureForm.
• 3+ years with DevOps, Kubernetes/containerization, and technologies such as Argo CD or Argo Workflows.
• 3+ years of enterprise application integration experience; Guidewire or Salesforce is highly desirable.
• 4+ years with data platforms such as Snowflake, Amazon Redshift, or BigQuery.
• 2+ years working with GenAI/LLM technologies such as OpenAI or Gemini.
• 1+ year with Agentic AI frameworks such as LangGraph, AutoGen, or Google ADK.
• 3+ years of API orchestration experience with MuleSoft or Google Cloud API technologies.
• Strong experience with AWS, Azure ML, Kubernetes, Apache Beam, and enterprise architecture.

Architecture Experience

• 3+ years with Data Mesh Architecture and Data Product Design.
• 3+ years with Event-Driven Architecture (EDA).
• 4+ years designing scalable AWS ML/AI infrastructure in multi-tenant SaaS environments.
• 3+ years developing data architecture guidelines and standards.
• 3+ years of security experience in distributed systems.
• 4+ years designing scalable, decoupled enterprise systems.
• 5+ years of technology strategy and roadmap development.
• Strong ability to influence architecture decisions using data-driven insights.

Preferred Domain Experience

• 4+ years of functional knowledge in Insurance, including Policy, Claims, and Services Operations.
• 2+ years of experience with insurance legal, regulatory, and compliance requirements.
• 3+ years of data product development for functional business domains.
• 2+ years of experience delivering AI-driven business process automation.
• Experience with Guidewire integrations is highly preferred.

Key Skills

Education

Any Graduate