AI & Multi-Cloud Architecture Lead

Responsible for defining and advancing a cloud-agnostic, AI-enabled architecture strategy that supports enterprise analytics, automation, and operational decision-making across multi-cloud environments. This role leads architecture standards and governance across AWS and Google Cloud Platform while actively delivering hands-on prototypes, data pipelines, and AI integrations to accelerate adoption.

Operating as a shared services architecture function, this role both guides and demonstrates best practices—bridging strategy and execution to ensure scalable, cost-efficient, and production-ready solutions aligned with ServiceNow CMDB/APM and Apptio models.

Core Role Identity

Dimension      Expectation

Architecture   Defines standards, patterns, governance

Delivery            Builds POCs, pipelines, and AI integrations

Model  Shared service / enterprise enablement

Authority          Influences demonstrates (not just advises)

Cloud  Multi-cloud, cloud-agnostic mindset

Key Responsibilities

1. Multi-Cloud Architecture & Governance

• Define and implement cloud-agnostic architecture patterns across AWS and Google Cloud Platform
• Standardize Google Cloud Platform governance aligned to AWS controls
• Establish reusable reference architectures for data, AI, and infrastructure
• Promote abstraction via:
• Containers (Kubernetes)
• APIs
• Infrastructure as Code (Terraform)

2. Hands-On Enablement (POCs & Pipeline Delivery)

• Build proof-of-concept solutions to validate architecture patterns
• Develop and optimize data pipelines and integrations across systems (ServiceNow, Apptio, Jira)
• Implement AI-enabled workflows (model integration, automation)
• Provide hands-on support to delivery teams to accelerate adoption
• Translate architecture into working, scalable solutions

3. AI Integration & MLOps Enablement

• Design and implement AI-ready pipelines (structured unstructured data)
• Support:
• Model integration into enterprise workflows
• MLOps lifecycle enablement (CI/CD, monitoring, governance)
• AI tool/vendor evaluation
• Mature organization from:
• POCs → Embedded AI → Governed enterprise AI

4. Data Architecture & Integration (CMDB/APM-Aligned)

• Architect data flows integrating:
• ServiceNow (CMDB/APM)
• Apptio (cost transparency)
• Jira (delivery data)
• Address key challenges:
• Data latency
• Data duplication
• Cost visibility gaps
• Enforce system-of-record and data ownership principles

5. Governance & FinOps (Advisory Enablement)

• Define standards for:
• Cloud cost optimization (FinOps)
• AI governance and lifecycle management
• Data quality and pipeline SLAs
• Support KPI transparency:
• Cloud cost per application
• Data pipeline reliability
• AI ROI
• Guide teams while enabling them through working solutions

6. Platform Strategy & Shared Services Leadership

• Act as a central architecture leader and enabler
• Support teams through:
• Architecture reviews
• POC delivery
• Design guidance
• Build reusable enterprise assets:
• Patterns
• Templates
• Integration frameworks

Required Experience

• 7 years in cloud architecture, data engineering, or infrastructure
• Proven experience in multi-cloud environments (AWS Google Cloud Platform)
• Demonstrated ability to:
• Design architecture and deliver working solutions
• Build data pipelines and integrations
• Strong experience with:
• Python, SQL
• ETL/ELT pipelines
• Infrastructure as Code (Terraform preferred)
• Containers (Kubernetes)

AI & Modern Architecture Requirements

• Hands-on experience with:
• AI/ML integration into enterprise pipelines
• MLOps or AI lifecycle tooling
• Experience evaluating and implementing:
• AI platforms
• Automation tooling

Preferred Experience

• ServiceNow CMDB/APM integration
• Apptio (cost allocation / FinOps)
• Experience solving:
• Cross-system duplication
• Data lineage challenges
• Exposure to Generative AI integration

Success Metrics (Aligned to Your KPIs)

• Reduction in cloud cost per application
• Improvement in pipeline SLAs
• Reduction in duplicate data/integrations
• Increase in production AI-enabled workflows
• Adoption of multi-cloud architecture standards
• Number of successful POCs transitioned to production