AI & Multi-Cloud Architecture Lead

Role Summary:

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