ML Ops Architect @ Farmers Insurance
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Job Details
About Farmers Insurance
Farmers Insurance is more than just your favorite commercials. We deliver peace of mind to our customers through protection, comprehensive advice, and support in moments of truth. Our high-performance culture is focused on results, accountability, and continuous growth, embracing collaboration, creativity, and innovation.
Work Arrangement
This ML Ops Architect role is based in a hybrid work environment. Employees work at least three days in office (if within 50 miles of a Farmers corporate office) and up to two days virtually. Remote applicants beyond 50 miles may still be considered.
Job Summary
The ML Ops Architect designs and implements scalable AWS ML/AI cloud infrastructure in a multi-tenant SaaS environment. The role collaborates with data scientists, engineers, and IT teams to define requirements, deploy ML models, and integrate ML pipelines with enterprise architectures.
- Architect and implement AWS ML/AI cloud infrastructure.
- Collaborate on ML model development and deployment.
- Recommend tools and ensure alignment with enterprise standards.
- Oversee Kubernetes management and ML pipeline orchestration.
- Mentor technical teams on ML Ops best practices.
Requirements & Experience
Applicants should have significant enterprise architecture experience along with expertise in AI/ML strategy, MLOps tools (AWS Sagemaker, GCP Vertex AI, Databricks), containerization using Kubernetes, and integration experience including Guidewire. Familiarity with data orchestration tools like Apache Airflow, Kubeflow, and security practices in distributed systems is required. A Bachelor’s degree is required and TOGAF certification is preferred.
Key Skills/Competency
- AWS ML/AI
- MLOps
- Enterprise Architecture
- Kubernetes
- Cloud Infrastructure
- Data Pipelines
- Guidewire
- DevOps
- TOGAF
- AI/ML Strategy
How to Get Hired at Farmers Insurance
🎯 Tips for Getting Hired
- Tailor your resume: Highlight ML Ops and cloud architecture skills.
- Research Farmers Insurance: Understand their mission and work culture.
- Emphasize experience: Focus on enterprise architecture and AWS deployment.
- Prepare technical case studies: Discuss ML pipeline integrations and Kubernetes experience.