AI & Multi-Cloud Architecture Lead
- Quantum World Technologies Inc.
3 Days Ago
NA
W2, C2C
Palm Beach-FL
7-18 Years
Required Skills: Python, SQL, ETL/ELT pipelines, AI/ML integration into enterprise pipelines
Job Description
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
