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AI & Multi-Cloud Architecture

Job

Avance Consulting

West Palm Beach, FL (In Person)

Full-Time

Posted 3 days ago (Updated 1 day ago) • Actively hiring

Expires 7/1/2026

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Job Description

AI & Multi-Cloud Architecture at Avance Consulting AI & Multi-Cloud Architecture at Avance Consulting in West Palm Beach, Florida Posted in about 22 hours ago.
Type:
full-time
Job Description:
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 d 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 & d 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