AI & Multi-Cloud Architecture Lead

Cloud-Agnostic AI-Enabled Architecture Strategy 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 GCP 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 GCP. Standardize GCP 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 + GCP) 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 PamTen