Overview
On Site
Full Time
Part Time
Accepts corp to corp applications
Contract - Independent
Contract - W2
Skills
Collaboration
Cloud Computing
Enterprise Architecture
Computer Cluster Management
Management
Mentorship
Roadmaps
Machine Learning Operations (ML Ops)
Amazon SageMaker
Vertex
Extract
Transform
Load
Apache Airflow
Databricks
DevOps
Continuous Delivery
Workflow
Kubernetes
Enterprise Application Integration
Guidewire
Salesforce.com
Snow Flake Schema
Amazon Redshift
Generative Artificial Intelligence (AI)
Autogen
Orchestration
MuleSoft
Google Cloud
Google Cloud Platform
API
Product Design
Data Analysis
EDA
Amazon Web Services
Machine Learning (ML)
IaaS
SaaS
Data Architecture
Legal
Regulatory Compliance
Insurance
Product Development
Artificial Intelligence
Process Automation
Job Details
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, 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
" 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, 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
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.