ML Ops Enterprise Architect

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PTN_US_GBAMSREQID_CandidateBeelineID
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Bill Rate: $80
MSP Owner: Subhashree samal
Location: - US Remote
Duration: 6 months
GBaMS ReqID: 11005744
Job Title: ML Ops Enterprise Architect

Job Summary
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.
(Position Title Enterprise Architect)

Essential Job Functions
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.

Experience 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, 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.

Education Requirements
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.
Additional Qualifications


Role Descriptions: 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.
Essential Skills: Job Title: ML Ops Enterprise ArchitectData & Analytics Technology 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). 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.
Desirable Skills:
Keyword:
Skills: Digital : Machine Learning~Digital : DevOps~AIOps~Enterprise Architecture
Experience Required: 10 & Above