AI & Multi-Cloud Architecture Lead

About US

Texnere is built on the belief that human potential and organizational intelligence can be intentionally architected, scaled, and sustained. We harmonize human intelligence with artificial intelligence to design organizations that think, adapt, and grow with purpose.

Job title : AI & Multi-Cloud Architecture Lead

Job Location : Palm Bach Florida , (Onsite )

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

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