01
Engagement
Amazon Connect routes member conversations and exposes real-time agent metrics.
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AWS / GenAI Platform
Secure GenAI Member Support on AWS
An end-to-end AWS platform for conversational AI and AI-assisted support in a regulated environment.
Built and operated the cloud foundation for GenAI-enabled member support workflows, connecting Amazon Connect, event-driven services, container workloads, data stores, and operational guardrails.
- Role
- Senior Cloud / DevOps Engineer
- Engagement
- Enterprise cloud and GenAI delivery
- Context
- Regulated healthcare
- Focus
- GenAI enablement and platform reliability
The challenge
Making the platform useful, secure, and operable from day one.
Member-facing support teams needed faster, more intelligent service experiences without weakening security, auditability, or availability. The platform had to support real-time conversations, AI-assisted workflows, and multiple integration paths while still meeting the controls required in a regulated healthcare environment.
01
AI-enabled services required governance, approval, and audit alignment.
02
Conversational support needed secure integrations with enterprise systems.
03
The workload ran across multiple environments and needed repeatable releases.
04
Operations had to cover cloud and on-premises dependencies.
05
Manual server lifecycle work could not keep up with the platform.
End-to-end architecture
How the platform connects from user action to operational control.
Scroll horizontally to follow the complete architecture flow.
How I built it
Delivery work from foundation to production operations.
Standardized AWS foundations
Provisioned VPC, IAM, compute, storage, and database patterns with Terraform, CloudFormation, and Ansible across multiple environments.
Event-driven AI integration
Connected service workflows with Lambda, Kinesis, SQS, and SNS so support events could move without tight coupling.
Container delivery
Packaged services with Docker and delivered them through Jenkins, ECS, Kubernetes, and CodeDeploy with controlled release paths.
Responsible AI guardrails
Built environment standards for secrets, logging, IAM boundaries, approvals, and auditability before AI workloads reached production.
Reliability engineering
Automated server lifecycle and recovery, applied patching, and used CloudWatch, Prometheus, Grafana, and Kafka metrics to find issues early.
What changed
Outcomes that made the platform easier to trust and operate.
AI support ready
Created a secure platform foundation for conversational AI and AI-assisted member service workflows.
Governance built in
Aligned infrastructure and releases with enterprise approval, governance, and audit expectations.
Less manual work
Automated infrastructure creation, server rehydration, deployment, and recovery with IaC and configuration management.
Clear operations
Connected platform metrics, application events, and dashboards so teams could trace health and upstream and downstream traffic.
Technology stack
Tools used across the platform.
AWS VPCEC2ECSECRLambdaS3EFSRDSKinesisSQSSNSCloudWatchIAMAmazon ConnectTerraformCloudFormationAnsiblePythonDockerKubernetesJenkinsPrometheusGrafanaKafka
Lessons
What I would carry into the next platform.
AI platforms need the same operational discipline as any regulated production system.
Security and audit guardrails work best when they are part of the platform baseline.
Automating recovery and server lifecycle removes a major source of operational risk.
Observability has to connect application behavior with the cloud and network layer.
Next case study
GCP Platform for Conversational AI and GenAI Workloads
Designed GCP infrastructure and delivery patterns using GKE, Cloud Run, Pub/Sub, and BigQuery while hardening IAM, secrets, audit logging, and environment controls.