About the Role :

The engagement is focused on establishing a governed Enterprise AI Platform on AWS that enables customers to securely build, deploy, govern, and operate AI agents at scale.

Key Deliverables :

- A production-ready Agentic AI Platform built on AWS AgentCore and Amazon Bedrock.

- Enterprise-grade security, governance, identity management, policy enforcement, guardrails, auditability, and kill-switch controls to safely operationalize AI workloads.

- Integration framework using MCP (Model Context Protocol) to connect AI agents with enterprise systems such as ServiceNow, Microsoft 365, SuccessFactors, Legal repositories, and future business applications.

- A complete AI Platform Engineering and DevOps capability, including Infrastructure as Code, CI/CD pipelines, version control, rollback mechanisms, observability, and FinOps controls.

- A Citizen Developer Enablement Model leveraging Kiro and Quick Suite, allowing business users to rapidly create governed AI solutions while inheriting enterprise security and compliance standards.

- End-to-end observability and operational visibility through CloudWatch, OpenTelemetry, CloudTrail, dashboards, cost tracking, and governance controls.

- Initial deployment of business-aligned AI use cases to validate platform capabilities and accelerate enterprise adoption.

- Training, knowledge transfer, and operational enablement to build internal capability and establish a self-sustaining AI platform team.

Key Roles Required :

- AWS AI/Cloud Architect and POD Lead : Focus on AWS AI landing zone architecture, security, governance, customer alignment, and technical delivery.

- AWS AgentCore Engineer : Build the AgentCore foundation, agent runtime, registry, scaling, lifecycle management, and Bedrock integrations.

- AWS Platform Engineer : Develop reusable CDK/IaC templates, VPC and platform services, deployment patterns, and environment automation.

- AI Integration Engineer : Implement MCP and enterprise-system integrations, agent workflows, guardrails, identity integration, and use-case onboarding.

- Senior DevOps/MLOps Engineer : Establish CI/CD, evaluation gates, observability, cost controls, versioning, rollback, and production-readiness controls.