ML Ops Enterprise Architect

Farmers Insurance Careers

81d•$156,300 - $297,125•Hybrid

About The Position

This horizontal role defines and governs technology strategy that supports multiple business units and domains across the organization. This role acts as the critical link between business strategy and technology execution for the entire portfolio. It operates with broad autonomy and complexity under the guidance of senior team members, influencing executive decisions, shaping strategic roadmaps, and leading initiatives that span platforms and technologies. Designs and develops IT architecture strategy, standards and roadmap while creating Enterprise architecture delivery (integrated process, applications, data and technology) in alignment with Enterprise architecture vision and direction. Requires specialized depth and/or breadth of expertise in Enterprise Architecture or related field. Interprets internal/external business challenges and recommends best practices to improve products, processes or services.

Requirements

Minimum ten years experience across architecture disciplines with significant enterprise architecture leadership 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).
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.
High School Diploma or equivalent required.
Bachelors degree preferred.
Masters degree preferred.
Architect or senior-level industry certifications required upon hire.
Second architect or senior-level industry certification required within 12 months of hire.
TOGAF Certified EA Architect preferred.
Ability to drive multiple projects to successful completion.
Proven experience leading and delivering enterprise-wide architectural initiatives that resulted in measurable business outcomes (example, cost savings, improved agility, reduced risk) required.
Ability to influence internal and/or external constituents.
Demonstrated experience working with and influencing senior executive leaders on enterprise-level technology strategy required.

Nice To Haves

Familiarity with Guidewire integrations is highly desirable.
4+ years: Functional Knowledge of Insurance Domains (Policy, Claims, Services Ops) - Preferred.
2+ years: Legal & Compliance Regulations in Insurance - Preferred.

Responsibilities

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.