Principal Cloud Architect

Career Techniques; Dallas (TX); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Career Techniques is seeking a Principal Cloud Architect to own the technical direction of our public cloud platforms across AWS and Azure. You will set architecture standards, governance controls, and guide engineering teams from discovery through production, ensuring scalable, secure platforms with AI tooling integrated as a quality gate.

You will design landing zones, multi-cloud networks, data platform architectures, and AI workflows, mentor engineers, participate in architecture reviews,

Qualifications

• Experience translating CAF/Well-Architected into concrete controls.
• Strong governance and policy-as-code capabilities.
• Experience with multi-account AWS and multi-subscription Azure.

Responsibilities

• Define cloud architecture standards across AWS and Azure.
• Architect multi-cloud landing zones and hub-and-spoke networks.
• Lead data platform architecture and governance.
• Design AI inference gateway and agentic AI workflows.
• Mentor engineers and participate in architecture reviews.

Skills

AWS & Azure cloud architecture

Kubernetes (AKS/EKS)

Cloud networking

Terraform IaC

Policy-as-code & governance

Leadership without authority

Technical communication

Education

Bachelor's or Master's degree in Computer Science or Engineering

CCNA or equivalent networking certification

Tools

Terraform

AKS/EKS

Azure Policy

AWS SCPs/RCPs

Job description

We are looking for a Principal Cloud Architect to own the technical direction of our public cloud platforms. RYou will set architecture standards, design governance controls, and guide engineering teams in turning them into production-grade platforms.The role spans the full architecture lifecycle. That covers discovery and design, implementation guidance, platform operationalization, and the documentation and enablement that let platforms scale beyond the people who built them. You will work across managed Kubernetes, cloud networking, security, and data platform architecture. You will also shape how AI tooling is embedded in engineering: not as a productivity add-on, but as a quality gate in the delivery pipeline.

What You'll Do

Architecture Standards & Governance

• Define cloud architecture standards, reference architecture, and design principles across AWS and Azure
• Design and enforce technical guardrails across multi-account AWS and multi-subscription Azure environments, using policy-as-code and compliance controls (e.g., AWS SCPs/RCPs, Azure Policy)
• Translate industry frameworks (e.g., CAF, Well-Architected) into concrete technical controls and patterns
• Design the tagging and cost-attribution architecture that underpins FinOps reporting
• Present architecture direction, trade-offs, and technical risk to senior leadership alongside the Director

Platform Architecture

• Architect multi-cloud landing zones and hub-and-spoke network topologies across AWS and Azure
• Design Kubernetes platforms at scale on managed services (e.g., AKS, EKS), including cluster lifecycle, multi-tenancy, and workload scheduling
• Lead cloud network architecture: inter-region and cross-cloud routing, private DNS, private endpoints, and connectivity to corporate networks (e.g., ExpressRoute, Direct Connect)
• Integrate batch and workflow orchestration on Kubernetes with cloud-native service networking and modern workload identity patterns

Data Platform Architecture

• Design cloud data platforms using layered (e.g., medallion) architecture across ingestion, transformation, and consumption
• Enable distributed querying across curated data sources for engineering and analytical users
• Define data access control, governance, and observability patterns

AI & Emerging Capabilities

• Architect AI inference gateway infrastructure that provides centralized model routing, security controls, cost attribution, and observability
• Design self-hosted, open-weight model inference on cloud GPU capacity, so AI workloads can run against sensitive data without exposure to third-party model providers
• Design agentic AI workflows for engineering (deployment review, IaC PR validation, platform operations) that act as quality gates in the delivery pipeline

Technical Leadership & Enablement

• Serve as the technical escalation point for the hardest platform problems
• Mentor engineers through design reviews, pairing, and architecture guidance
• Run technical workshops on new architecture patterns, tools, and practical AI workflows
• Contribute to technical interviews and set the technical bar for platform roles

Technical Delivery

• Own Infrastructure as Code standards (Terraform), including bringing existing cloud resources under version-controlled IaC and enforcing repeatable deployment patterns
• Define DevSecOps pipeline patterns and identity and security architecture
• Produce architecture documentation (high-level and low-level designs) and runbooks that let others operate and extend the platforms

What You'll Bring

• Career progression from hands-on infrastructure or cloud engineering into principal-level cloud architecture
• Production depth across AWS and Azure: landing zones, multi-account/subscription governance, managed services, and security architecture
• Managed Kubernetes at scale (AKS, EKS), including cluster lifecycle management and workload scheduling
• Cloud networking: hub-and-spoke topologies, peering, private DNS, private endpoints, and hybrid connectivity
• Terraform fluency in complex, multi-account environments
• A track record of designing and enforcing cloud governance: guardrails, policy-as-code, and compliance controls
• Influencing engineering teams and senior stakeholders without direct authority
• Clearly communicating architecture decisions and trade-offs, both written and presented

Certifications (one or more expected)

• Microsoft Certified: Azure Solutions Architect Expert
• AWS Certified Solutions Architect (Associate or Professional)

Optional

• Cloud HPC for scientific or engineering workloads, including job schedulers and high-speed interconnects (e.g., InfiniBand)
• Cloud data platform delivery at production scale (e.g., medallion architecture, AWS data services)
• AI inference gateway or model serving infrastructure
• Agentic AI workflows applied to infrastructure engineering
• GPU infrastructure or ML platforms in the cloud
• Regulated or high-availability environments (e.g., financial services, motorsport, government)
• A degree in Engineering, Computer Science, or a related field, or equivalent experience
• CCNA or equivalent network engineering foundation