Rahul LoreSoftware Architect

Healthcare · Distributed Systems · Edge · Applied AI

Designing reliable systems for complex, real-world problems.

Software Architect with 20+ years of experience designing distributed healthcare platforms, high-availability edge systems, enterprise applications, and practical AI solutions. I bridge architectural thinking with hands-on engineering to build systems that are resilient and maintainable.

Explore my work View résumé
01 / Selected Engineering Work

Architecture proven through implementation.

Three systems that show how I approach clinical data flow, edge reliability, real-time distribution, and the disciplined use of AI in business software.

High-Availability Clinical Data Integration

A local edge architecture for collecting measurements from connected medical devices, consolidating them reliably, and delivering standards-based messages to hospital systems.

  • ProblemContinuous device measurements must remain available despite constrained hospital-edge infrastructure.
  • DecisionSeparate device collection, aggregation, high availability, storage, and HL7 delivery into explicit responsibilities.
  • ValueDependable local processing with failover and a seven-day rolling data window.

Architecture

Raspberry Pi collectors connect to bedside devices and forward measurements to dedicated aggregation nodes. Keepalived provides virtual-IP failover between redundant nodes; the aggregation application performs the data processing.

Data handling

High-frequency vital-sign readings can be summarized into configurable reporting intervals—for example, one median heart-rate value from 60 readings. Waveform streams remain distinct from this aggregation path.

Raspberry PiLinux.NETDockerKeepalivedHL7Edge Computing

Real-Time Medical Device Data Distribution

A distributed flow for making live patient vitals and waveform data from multiple medical devices available to authorized clinical applications.

  • ProblemBedside telemetry originates across heterogeneous devices and must reach multiple application surfaces.
  • DecisionUse edge acquisition and integration services with event-driven messaging and a separate distribution layer.
  • ValueA clear path from medical equipment to desktop, tablet, and mobile consumers.

System flow

Medical equipment produces patient vitals and waveforms. Edge devices collect bedside telemetry, while integration services consolidate inputs from multiple edge sources.

Distribution

Kafka supports event-driven streaming across services and SignalR supports real-time delivery to connected application experiences where those patterns fit the flow.

C#.NETRaspberry PiKafkaSignalRDistributed ServicesReal-Time Telemetry

Applied AI in a Full-Stack Business Application

An independently developed customer-and-order application exploring how AI can augment ordinary business workflows without replacing predictable application behavior.

  • Core applicationCustomer and order management backed by a conventional database and service architecture.
  • AI layerNatural-language SQL assistance, Semantic Kernel integration, embeddings, and AI-assisted search.
  • DeliveryContainerized services with an automated CI/CD workflow.

Engineering approach

The design keeps core business workflows deterministic while treating model-assisted features as bounded capabilities inside a familiar frontend, API, and database architecture.

Scope

The application demonstrates end-to-end engineering decisions across interface design, backend services, data access, AI orchestration, deployment, and operations.

.NETNuxtSQL ServerSemantic KernelOpenAIEmbeddingsDocker
Explore live application
02 / Architecture & Engineering Expertise

Depth where systems meet the real world.

Experience that spans architecture and implementation, organized around the problems it helps solve.

Distributed Systems

Event-driven architecture, messaging, service integration, scalability, resiliency, and practical modernization.

Healthcare & Edge

Medical device integration, clinical telemetry, HL7, Linux, Raspberry Pi, and edge deployments.

Backend & Cloud

C#, .NET, ASP.NET Core, SQL Server, Azure, Docker, observability, and CI/CD.

Applied AI

Semantic Kernel, LLM integration, embeddings, retrieval-augmented generation, and AI-assisted workflows.

03 / Professional Experience

Two decades of building and evolving critical software.

A progression from hands-on application engineering to architecture leadership in healthcare and distributed systems.

Talis Clinical

Software Architect

Architecture for distributed healthcare systems, patient telemetry, medical device integration, highly available edge services, and containerized delivery.

CitiusTech

Solution Architect

Healthcare application modernization, .NET architecture, anesthesia platform work, and technical guidance across development teams.

Capgemini

Senior Consultant

Reusable .NET frameworks, database performance, caching, and application performance improvements.

Bank of America Merrill Lynch

Analyst

Enterprise .NET applications, reusable components, multithreaded processing, and performance-oriented application frameworks.

04 / Contact

Let’s build systems that last.

For architecture leadership, healthcare platforms, distributed systems, or thoughtful AI integration, I’m always interested in a substantive engineering conversation.

EmailLinkedInRésumé