AI/ML Solutions Architect Cloud Solutions Architect C2c jobs

Job Location : Palm Bach Florida , (Day1 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 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

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