Job description

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 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

Requirements

Must-haves

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
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
Containers (Kubernetes)
Infrastructure as Code (Terraform)
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
AI Integration & MLOps Enablement
Design and implement AI-ready pipelines (structured + unstructured data)
Model integration into enterprise workflows
MLOps lifecycle enablement (CI/CD, monitoring, governance)

Nice-to-haves

Infrastructure as Code (Terraform preferred)

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiawsgcpkubernetesjiraci/cdpythonsqlml

Platform: jobdiva
Effort: medium