AI & Platform Engineering
Regulated Clinical AI Platform
A private engagement focused on architecting and operating a production environment for an AI-native clinical research platform. The work combined cloud security, reliability, GenAI workflows, backend services, observability and controlled delivery in a compliance-aware context.
- Role
- Cloud Architect & AI Engineer
- Period
- 2025 - present
- Context
- Private client work
AWSGoogle CloudVertex AIAmazon BedrockCloud RunWAFIAMKMSOpenTelemetryNext.jsTypeScriptPostgreSQL
Constraints
- Clinical research workflows with sensitive data, role-based access and auditability requirements.
- Production use cases involving document processing, retrieval-augmented generation and AI-assisted operations.
- Need for practical reliability and security controls without turning delivery into a heavyweight process.
What I owned
- Designed and operated the cloud environment across AWS and Google Cloud services.
- Implemented managed-model integrations for Gemini and Bedrock-backed workflows.
- Built backend and web application services with authentication, RBAC, CI/CD and Infrastructure as Code.
- Set up structured logging, tracing and operational visibility for production support.
Engineering decisions
- Cloud Run services behind global load balancing and web application firewall controls.
- Least-privilege IAM, secrets management and encrypted infrastructure components.
- RAG and document-processing flows for clinical operations support.
- Compliance-aware delivery aligned with GDPR, GxP, ICH GCP and 21 CFR Part 11 considerations.
Outcome
- Established an operable production environment spanning cloud architecture, application delivery and managed-model workflows.
- Made security, release and operational behavior explicit through Infrastructure as Code, telemetry and controlled access patterns.
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