AI & Multi-Cloud Architecture

Title- AI & Multi-Cloud Architecture Lead (Hands-On)

Location- Juno Beach, FL - Onsite

(Architecture + Enablement + Proof Delivery)

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:

o Containers (Kubernetes)

o APIs

o 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:

o Model integration into enterprise workflows

o MLOps lifecycle enablement (CI/CD, monitoring, governance)

o AI tool/vendor evaluation

• Mature organization from:

o POCs → Embedded AI → Governed enterprise AI

4. Data Architecture & Integration (CMDB/APM-Aligned)

• Architect data flows integrating:

o ServiceNow (CMDB/APM)

o Apptio (cost transparency)

o Jira (delivery data)

• Address key challenges:

o Data latency

o Data duplication

o Cost visibility gaps

• Enforce system-of-record and data ownership principles

5. Governance & FinOps (Advisory + Enablement)

• Define standards for:

o Cloud cost optimization (FinOps)

o AI governance and lifecycle management

o Data quality and pipeline SLAs

• Support KPI transparency:

o Cloud cost per application

o Data pipeline reliability

o 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:

o Architecture reviews

o POC delivery

o Design guidance

• Build reusable enterprise assets:

o Patterns

o Templates

o Integration frameworks

Required Experience

• 7+ years in cloud architecture, data engineering, or infrastructure

• Proven experience in multi-cloud environments (AWS + GCP)

• Demonstrated ability to:

o Design architecture and deliver working solutions

o Build data pipelines and integrations

• Strong experience with:

o Python, SQL

o ETL/ELT pipelines

o Infrastructure as Code (Terraform preferred)

o Containers (Kubernetes)

AI & Modern Architecture Requirements

• Hands-on experience with:

o AI/ML integration into enterprise pipelines

o MLOps or AI lifecycle tooling

• Experience evaluating and implementing:

o AI platforms

o Automation tooling

Preferred Experience

• ServiceNow CMDB/APM integration

• Apptio (cost allocation / FinOps)

• Experience solving:

o Cross-system duplication

o 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