AI ML Ops Enterprise Architect

Purple Drive Technologies LLC

Culver City, CA, United States

25 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience required

1 year minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Airflow Amazon Web Services Data Analysis Applications Architecture BigQuery Software as a Service Cloud Computing Data Architecture DevOps Distributed Systems

+21 more

Machine Learning Octopus Deploy Salesforce.Com Enterprise Application Integration Google Cloud Large Language Models Snowflake Generative AI Event Driven Architecture Containerization Kubernetes Data Analytics Data Management Machine Learning Operations Virtual Agents Artificial Intelligence Markup Language (AIML) Data Pipelines Guidewire Mulesoft Amazon Redshift Databricks

Job 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

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, Google Cloud Platform 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.”

CONTRACTOR, FULL_TIME, PART_TIME