Triangle Health · 2025 – Present
Multi-agent AI healthcare platform
End-to-end architecture for an AI-powered healthcare platform — conversational agents, clinical workflows, FHIR interoperability, and production-grade orchestration.
Role: Principal Software Engineer
Problem
Healthcare teams need AI that can participate in real clinical workflows — collecting data, analyzing documents, orchestrating steps — while meeting interoperability, consent, and compliance requirements. Generic chatbots break down when tools, identity, and auditability matter.
Role
Technical leader for design and implementation across frontend, backend, AI infrastructure, and cloud. Established scalable engineering patterns adopted across the platform and mentored engineers on AI integration and system design.
Constraints
- Regulatory and privacy expectations around patient data and consent
- Need for specialized agents rather than a single monolithic prompt
- Interoperability with healthcare systems via HL7 FHIR and SMART on FHIR
- Production observability for distributed AI services
- Human-in-the-loop decision points for clinical safety
Approach
- Architected a multi-agent platform using GPT-5.x, MCP, and LangGraph for patient onboarding, clinical data collection, document analysis, and workflow orchestration.
- Built an extensible MCP server ecosystem exposing healthcare tools and clinical capabilities through standardized interfaces.
- Designed production AI pipelines covering prompt engineering, tool calling, structured outputs, retrieval workflows, and HITL controls.
- Implemented consent, authentication, authorization, and secure data-sharing for patient-facing AI applications.
- Established OpenTelemetry + Datadog observability with structured logging, metrics, and tracing for AI services.
- Used event-driven cloud-native backends with asynchronous processing and resilient messaging.
Agent specialization
Rather than one general assistant, specialized agents own onboarding, data collection, document analysis, and orchestration — coordinated through LangGraph with clear tool boundaries via MCP.
Interoperability without shortcuts
SMART on FHIR and OAuth-backed access patterns keep AI-assisted workflows aligned with healthcare identity and data-sharing norms, instead of brittle one-off integrations.
Outcomes
- Unified architecture spanning conversational AI, clinical workflows, and healthcare interoperability standards.
- Reusable MCP tool surfaces and SDKs that accelerate feature development across teams.
- Engineering standards for AI evaluation, testing, deployment automation, and operational excellence.
- Close collaboration with product, clinical, and executive stakeholders to turn requirements into scalable systems.
Stack
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