We’re looking for a Cloud Architect to help design, build, and continuously improve the multi-cloud foundation that powers our internal technology ecosystem. This role sits within our IT Infrastructure & Operations organization and partners closely with Security, Enterprise Architecture, Application teams, and business stakeholders.

Requirements:

• 5+ years in cloud/infrastructure engineering or architecture roles, with demonstrated ownership of enterprise-scale environments.

• Advanced working knowledge of AWS, Azure and GCP, including architecture, security, networking, and operations.

• Strong experience with cloud governance and foundational design, such as:

  • Multi-account/subscription/project strategies

  • IAM and identity federation patterns

  • Centralized logging/monitoring and policy guardrails

• Strong hands-on experience with Infrastructure as Code (Terraform preferred; CloudFormation/ARM/Bicep are also valuable).

• Experience implementing CI/CD for infrastructure and platform services (Git-based workflows, automated testing/validation, release discipline).

• Solid understanding of cloud networking concepts and designs (segmentation, routing, hybrid connectivity, DNS, load balancing).

• Proven ability to write and maintain clear architecture documentation (diagrams, decision records, standards, runbooks).

• Strong communication skills: able to explain complex technical choices, tradeoffs, and risk in a straightforward way to both technical and non-technical audiences.

• Working knowledge of cloud AI/ML services and common patterns.

Nice-to-Haves (Preferred Qualifications)

• Cloud certifications such as:

  • AWS Solutions Architect (Associate/Professional)

  • Microsoft Certified: Azure Solutions Architect Expert

  • Google Professional Cloud Architect

• Experience building/operating:

  • Kubernetes platforms (EKS/AKS/GKE), container security, and platform enablement

  • Serverless architectures and event-driven integrations

  • Enterprise observability (cloud-native + third-party tooling)

• Experience with cloud security and compliance frameworks (e.g., NIST, ISO 27001, SOC 2, PCI, HIPAA) and translating requirements into technical controls.

• Practical exposure to GenAI/ML platform patterns, such as:

  • Azure OpenAI / AWS Bedrock / Vertex AI

  • MLOps fundamentals (model lifecycle, monitoring, CI/CD for ML)

  • Secure RAG patterns, vector search concepts, data governance for AI

• Scripting/automation skills (Python, PowerShell, Bash, etc.).

• Experience contributing to (or leading) a Cloud Center of Excellence model, architecture review boards, or platform governance forums.

Responsibilities:

• Architect and evolve our multi-cloud strategy and reference architectures across AWS, Azure, and GCP—prioritizing AWS/Azure depth while ensuring GCP solutions are viable and well-governed.

• Design and implement cloud foundations / landing zones (accounts/subscriptions/projects, network topology, identity integration, logging, policy guardrails, and shared services).

• Establish cloud architecture standards that enable speed and consistency (security baselines, network patterns, naming/tagging, environment strategies, reusable modules/templates).

• Drive infrastructure automation using Infrastructure as Code (IaC) and CI/CD practices:

  • Build and maintain reusable Terraform modules (or equivalent)

  • Enable repeatable deployments for core cloud services and application teams

• Partner with Security to embed controls by design, including IAM strategy, key management, secrets management, encryption, vulnerability management, and cloud security posture practices.

• Design enterprise-grade cloud networking, including connectivity, segmentation, routing, DNS, load balancing, and hybrid connectivity (e.g., VPN/Direct Connect/ExpressRoute equivalents).

• Enable reliability and operational excellence by defining monitoring/alerting patterns, SLOs/SLIs, incident response integration, backup/DR strategies, and operational runbooks.

• Lead cost-aware architecture and FinOps practices, including tagging standards, cost allocation, budget/forecasting inputs, and optimization recommendations (e.g., commitments/reservations, right-sizing, lifecycle policies).

• Support and advise internal engineering teams on solution designs (containers/Kubernetes, serverless, data services, integration patterns), helping teams adopt cloud-native practices responsibly.

• Build AI readiness into our cloud platforms:

  • Help define guardrails for AI/ML services usage (data classification, access controls, audit, retention)

  • Partner with teams exploring GenAI/ML to ensure solutions are secure, scalable, and compliant

  • Stay current on cloud AI trends and translate them into pragmatic platform recommendations

• Mentor and elevate the team through technical leadership, design reviews, documentation, and knowledge sharing.