ML OPS ARCHITECT
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Job Location:
Woodland Hills, CA - USA
Monthly Salary:
Not Disclosed
Posted on:
2 hours ago
Vacancies:
1 Vacancy
Job Summary
Description:
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.
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
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.
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
Key Skills
- APIs
- Pegasystems
- Spring
- SOAP
- .NET
- Hybris
- Solution Architecture
- Service-Oriented Architecture
- Adobe Experience Manager
- J2EE
- Java
- Oracle
