Rahul Lore Software Architect • Principal Software Engineer • Applied AI & Distributed Systems
Open to software architecture and principal engineering roles
Rahul Lore

Software Architect • Principal Software Engineer • Distributed Systems, Healthcare Platforms, and Applied AI

Senior engineering leadership grounded in shipped systems, not just strategy.

I design and modernize production software for teams that need architecture depth, real delivery experience, and practical AI adoption. My background spans healthcare platforms, distributed .NET services, real-time telemetry, deployment automation, and applied AI workflows that stay connected to business logic.

Recent work includes Kafka- and SignalR-based streaming for patient vitals and waveform telemetry, modernization of legacy healthcare applications into distributed services, and a hosted .NET and Nuxt demo that shows how Semantic Kernel, embeddings, semantic search, and AI-assisted workflows can support real products without turning them into hype-driven experiments.
20+ years Engineering experience across architecture, modernization, and hands-on delivery.
Real-time systems Built healthcare telemetry and distributed backend flows where latency and reliability matter.
Applied AI Semantic Kernel, embeddings, semantic search, and AI-assisted workflow design.
Architecture story

A concise way to describe how I design production systems.

This section ties the portfolio message to scroll progress: editorial in tone, technical in content, and subtle enough to support the page rather than overpower it.

DISTRIBUTED SYSTEMS
HANDLE FAILURE
CONNECT DEVICES
MOVE DATA
AI, INTELLIGENTLY APPLIED
RELIABLE
Current signal

Current technical signal, framed like live system status instead of a skills cloud.

A quiet readout of the areas I am actively thinking about, building with, or connecting to practical product and platform work.

Agentic AI
Practical orchestration and workflow design
Distributed Systems
Resilience, state, and service boundaries
Edge Architecture
Latency-aware topology and device-adjacent design
Healthcare Technology
Operationally critical platforms and telemetry
.NET
Modern backend delivery and long-lived systems
RAG
Search-grounded AI tied to business logic
DISTRIBUTED SYSTEMS — EDGE — AI — HEALTHCARE — .NET — ARCHITECTURE — DISTRIBUTED SYSTEMS — EDGE — AI — HEALTHCARE — .NET — ARCHITECTURE —
Work experience highlights

Concrete engineering work with clear technical depth

These highlights keep the profile employer-facing while staying specific about shipped systems, platform work, and technical responsibility.

Real-time patient telemetry delivery

Architected Kafka- and SignalR-based streaming platforms that delivered patient vitals and waveform telemetry across clinical systems with low-latency message flow.

Applied AI demo built and hosted end to end

Built and hosted a .NET and Nuxt demo at rahullore.com/demo1 that uses Semantic Kernel, embeddings, semantic search, and AI-assisted workflows alongside deterministic business logic.

Healthcare platform modernization

Led the move from legacy healthcare applications toward cloud-native and event-driven services using Docker and distributed backend components.

Deployment automation and observability

Built containerized CI/CD pipelines for zero-touch deployments and implemented centralized logging with Seq and Serilog to improve production monitoring and troubleshooting.

Employer value

Why this profile feels credible to technical teams

The goal is to show seniority, substance, and current-market relevance without overstating the work or relying on generic buzzwords.

Specific distributed-systems work

Hands-on delivery in event streaming, resilient backend services, and real-time healthcare data movement.

Supported AI experience

Experience with Semantic Kernel, embeddings, semantic search, and AI-assisted workflows shown through a live hosted demo.

Operational focus

Work shaped by deployment reliability, observability, maintainability, and environments where uptime matters.

Senior execution

Ability to connect architecture decisions, delivery planning, and hands-on implementation without losing momentum.

Best fit

Strong fit for teams that need architecture judgment and working software.

Best aligned with organizations modernizing established platforms, shipping operationally critical systems, or adding practical AI capabilities that must work in real production environments.

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