Cloud Architect- AI Model

Responsibilities

• Design and optimization of AI-ready cloud environments: Modern AI workloads require specialized architectures, including high performance compute, GPU clusters, container orchestration, and distributed storage. An experienced architect ensures these environments are properly designed, cost efficient, and aligned with best practices.

• Integration of AI tools into existing systems: As AI tools become embedded across business processes, we need expertise in securely connecting models, APIs, vector databases, and pipelines to our existing applications and data sources.

• Support for model deployment and lifecycle management: Effective use of AI requires more than just model development. We need an expert who can manage deployment, monitoring, versioning, and scaling to ensure models operate reliably and safely in production environments.

• Governance, security, and compliance: AI systems introduce new risks related to data handling, model outputs, and access control. A Cloud Architect with AI expertise can implement guardrails, enforce cloud security policies, and ensure alignment with organizational, legal, and ethical standards.

• Cost management and operational efficiency: AI workloads can incur significant compute and storage costs if not properly architected. An experienced professional can implement automation, workload right sizing, and optimization strategies to reduce expenses.

• Support for innovation initiatives: As AI tools continue to evolve, this role will allow the organization to pilot new technologies, evaluate vendor solutions, and rapidly implement new capabilities without compromising security or stability.

Requirements

• Hands on experience creating and managing AWS IAM users, roles, policies, and permission sets.

• Deep understanding of Cloud Governance frameworks.

• Experience working within AWS enterprise environments, including services commonly tied to governance and IAM such as S3, CloudTrail, CloudWatch, ECR, and Bedrock.

• Practical AI/ML experience, preferably with cloud-native AI services (AWS Bedrock or similar), including understanding how IAM permissions impact model access, data security, and sandbox experimentation.

• Ability to evaluate and implement access control models, including role refinement, trust policy review, and development or enhancement of permission sets used across technical teams.

• Experience collaborating with cloud governance, security, and technical services teams to design, review, and improve identity-related workflows and ensure adherence to organizational standards.

• Strong documentation and communication skills, demonstrated through writing governance procedures, IAM configuration guides, and technical recommendations for stakeholders across IT and management.