Cloud Architect

DEW Softech Inc•Trenton, NJ

1d•$80 - $90•Remote

About The Position

Cloud Architect with AI experience who possesses both advanced cloud engineering skills and hands-on experience supporting, configuring, and optimizing AI models and tools. This role will provide value by designing and optimizing AI-ready cloud environments, integrating AI tools into existing systems, supporting model deployment and lifecycle management, ensuring governance, security, and compliance for AI systems, managing costs and operational efficiency of AI workloads, and supporting innovation initiatives by piloting new technologies and evaluating vendor solutions.

Requirements

Hands-on experience creating and managing AWS IAM users, roles, policies, and permission sets (3 Years)
Deep understanding of Cloud Governance frameworks (3 Years)
Experience working within AWS enterprise environments, including services commonly tied to governance and IAM such as S3, CloudTrail, CloudWatch, ECR, and Bedrock (3 Years)
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 (3 Years)
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 (3 Years)
Experience collaborating with cloud governance, security, and technical services teams to design, review, and improve identity-related workflows and ensure adherence to organizational standards (3 Years)
Strong documentation and communication skills, demonstrated through writing governance procedures, IAM configuration guides, and technical recommendations for stakeholders across IT and management (3 Years)

Responsibilities

Design and optimization of AI-ready cloud environments, including high performance compute, GPU clusters, container orchestration, and distributed storage.
Ensure AI cloud environments are properly designed, cost-efficient, and aligned with best practices.
Integrate AI tools into existing systems, securely connecting models, APIs, vector databases, and pipelines to applications and data sources.
Manage deployment, monitoring, versioning, and scaling of AI models in production environments.
Implement guardrails, enforce cloud security policies, and ensure alignment with organizational, legal, and ethical standards for AI systems.
Implement automation, workload right sizing, and optimization strategies to reduce AI workload expenses.
Pilot new technologies, evaluate vendor solutions, and rapidly implement new AI capabilities.