Software Architect

System Design for Scale, Reliability & Performance

I design distributed system architectures — from database schemas and event-driven pipelines to zero-downtime deployment strategies.

System DesignDistributed SystemsScalabilityInfrastructure

Technical Skills

What I bring as a Architect engineer

System Design

Domain-Driven Design— Bounded contexts & service boundaries
Event-Driven Architecture— Kafka topics & async event flows
API Contract Design— RESTful resource modeling & versioning
Multi-Tenant Architecture— Isolated tenant data & config

Data Architecture

PostgreSQL Optimization— Indexing, partitioning & query planning
MongoDB Geospatial— 2dsphere indexes & polygon queries
Redis Patterns— Caching, pub/sub & rate limiting
pgvector— Vector embeddings in relational store

Distributed Systems

Kafka Streaming— Topic partitioning & consumer groups
BullMQ Workers— Job scheduling with retry & DLQ
Microservices— NestJS modular service boundaries
Message Patterns— Saga, outbox & idempotency

Infrastructure

Docker— Multi-stage builds & compose stacks
Nginx— Reverse proxy, load balancing & SSL
Blue/Green Deploy— Zero-downtime with PM2 clusters
CI/CD Pipelines— GitHub Actions automated workflows

Architecture

How I architect architect systems

01

Requirements Analysis

Understand scale targets, data relationships, consistency requirements, and performance SLAs.

02

Architecture Blueprint

Design service boundaries, data flow diagrams, API contracts, and infrastructure topology.

03

Data Modeling

Schema design with proper indexing, normalization decisions, caching strategy, and migration planning.

04

Implementation Guidance

Define coding patterns, module boundaries, error handling strategies, and testing approaches.

05

Deployment Strategy

Zero-downtime deployment pipelines, monitoring, alerting, and capacity planning.

Featured Work

Production projects as a Architect engineer

AI SaaS PlatformLive in Production

Planfluence

Meetings into Momentum — AI Meeting Intelligence to Jira Backlog & Slack Recaps

Problem

Engineering discussions frequently end with action items and architectural commitments buried in unreviewed recordings or chat threads. Translating spoken consensus into Jira tickets with acceptance criteria wastes hours of developer and manager time.

Solution

Architected a 4-step pipeline: (1) Capture calls via Google Meet Chrome extension without intrusive bots; (2) Transcribe and extract decisions using Whisper and LLMs; (3) Review, approve, and publish tickets directly to Jira Cloud/Data Center and Trello backlogs; (4) Broadcast structured recaps to dedicated project Slack channels.

Architecture

  • Chrome Extension (Manifest V3) capturing tab audio locally with zero bots joining user meetings
  • Next.js 15 App Router web application with streaming AI summaries and interactive decision editor
  • High-speed Whisper transcription & structured JSON extraction for action items and acceptance criteria
  • Bi-directional Jira Cloud & Trello API integrations with one-click backlog and sprint publishing
  • Slack webhook automation broadcasting project-specific meeting recaps with deep links
  • Lemon Squeezy merchant-of-record subscription billing engine

Verified Results

Zero-bot audio capture eliminating attendee distraction and call overheadReduced post-meeting Jira ticket creation time from ~45 minutes to < 60 secondsSeamless bi-directional synchronization across Google Meet, Jira, Trello, and Slack
Next.js 15TypeScriptChrome ExtensionOpenAI Whisper & GPT-4oAtlassian Jira APISlack APILemon SqueezyTailwind CSS
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Local-First MobileLive in Production

GeniSteps

Hardware-Accurate Step Tracking & Vitality Companion — 100% Local-First

Problem

Mainstream pedometers drain device battery, lock essential 24-hour trends and data exports behind $39–$79/yr recurring subscriptions, and harvest sensitive biometric location and movement data to proprietary cloud servers.

Solution

Built a silicon-native motion engine with a 4th-order DSP bandpass filter (0.8–3.5 Hz) tuned to human gait biomechanics. Features silent lock-screen updates via >95% IPC throttling, natural stride pacing (~320ms), and 100% data sovereignty in on-device SQLite WAL mode.

Architecture

  • Silicon coprocessor integration: Apple CoreMotion (CMMotionActivityManager) & Android Sensor Hub
  • 4th-Order DSP digital signal processing filter (0.8–3.5 Hz) isolating true strides from vehicle vibration
  • Silent lock-screen live activity widgets with >95% IPC throttling for battery preservation
  • Local-first SQLite database running in WAL mode with instant 1-tap CSV and JSON spreadsheet export
  • Behavioral habit psychology engine: automated bankable streak freezes and rest-day scheduling
  • Standardized machine-readable /llms.txt factsheet for generative engine and AI model evaluation

Verified Results

< 1% daily battery overhead utilizing dedicated hardware coprocessors100% private: zero cloud servers, zero account creation, zero analytics tracking$0 forever: complete professional feature set with zero subscriptions or paywalls
React NativeApple CoreMotionAndroid Sensor HubSQLite (WAL)TypeScriptDSP FiltersiOS Live Activities
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B2B MarketplaceLive in Production

DispatchLink

B2B Freight Marketplace Connecting Carriers & Dispatchers

Problem

Freight dispatching is plagued by manual phone calls, untracked load confirmations, fragmented broker commissions, and slow escrow reconciliation.

Solution

Architected a unified real-time portal where carriers claim loads with automated geospatial radius search, live driver telemetry, and automated multi-tenant wallet payouts.

Architecture

  • Next.js 15 App Router with Server Components for low-latency freight searches
  • MongoDB 2dsphere indexes for polygon and radius origin-to-destination matching
  • MapLibre vector maps rendering live driver routes and telemetry coordinates
  • LemonSqueezy merchant-of-record integration for multi-party escrow billing

Verified Results

1,048+ matched freight dispatches processedReduced load confirmation turnaround by ~42% compared to manual brokersZero-drop webhook processing for payment and dispatch milestones
Next.js 15MongoDB GeospatialMapLibreLemonSqueezyTypeScriptTailwind CSS
View Live
Gen AI & VoiceLive in Production

Kookabe AI Suite

Enterprise WhatsApp RAG Chatbot & Automated AI Voice Mail

Problem

Companies received hundreds of repetitive WhatsApp voice notes, PDFs, and product inquiries daily, resulting in 4-8 hour response delays and missed sales leads.

Solution

Built a hybrid RAG system with pgvector and Claude 3.7 that parses customer inquiries, searches internal PDF documentation, and generates accurate answers with human-handoff triggers.

Architecture

  • WhatsApp Business API webhooks ingesting incoming text, voice notes, and document files
  • Hybrid RAG combining pgvector dense embeddings with BM25 keyword reranking
  • FastAPI backend handling asynchronous LLM streaming and conversation state
  • Automated audio transcription pipeline extracting lead data directly into CRM webhooks

Verified Results

Automated over 90% of routine inquiries with sub-3-second first responsesZero hallucinations verified via strict citation and source-chunk verificationContinuous 24/7 uptime handling high-volume customer messages
LangChainClaude 3.7FastAPIPostgreSQL / pgvectorWhatsApp Cloud APIRedis
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Experience

Where I've applied these skills

Founder & Lead Architect

atThe Geni SolutionsStudio & Products

2024 — Present

Founded and lead The Geni Solutions, an elite AI & software studio engineering autonomous LangGraph multi-agent workflows, enterprise RAG engines, and high-performance SaaS and mobile products.

  • Architected and shipped Planfluence (planfluence.site), an automated meeting-to-backlog platform with Google Meet Chrome extension capture, Whisper transcription, Jira/Trello automation, and Slack recaps.
  • Engineered GeniSteps (genisteps.thegenisolutions.com), a 100% local-first iOS & Android vitality companion leveraging silicon CoreMotion/sensor hubs, 4th-order DSP filters, and private SQLite WAL storage.
  • Standardized an agile 4-phase AI delivery process (Strategy & Mapping → Benchmark POC → Full Engineering → Guardrailed Deployment) delivering <200ms agent SLA and 99.4% retrieval accuracy.
  • Spearheaded multi-agent orchestrations, enterprise WhatsApp AI bots, and custom SaaS platforms across North America and global clients.
LangGraphLangChainNext.js 15PythonFastAPIPostgreSQL / pgvectorLocal-First SQLiteChrome ExtensionsDocker

AI & Full-Stack Engineer

atUpworkTop Rated

May 2026 — Present

Delivering production AI workflows, full-stack Next.js/NestJS platforms, and cloud DevOps systems for high-growth tech startups and international clients.

  • Consulting on AI agent architecture, hybrid RAG pipelines, and conversational voice systems.
  • Building scalable REST APIs, microservices, and database schemas with PostgreSQL and Prisma.
  • Maintaining a 100% Job Success Score with verified 5-star client contracts.
AI AgentsLangChainClaude 3.7Next.jsNestJSPostgreSQLDocker

Software Engineer

atDev House

May 2024 — Sep 2026

Engineered distributed message queues, asynchronous background worker pipelines, and backend microservice modules for enterprise clients.

  • Collaborated on Node.js and TypeScript microservice integrations with BullMQ queues.
  • Engineered decoupled background workers and Redis caching layers for high-throughput workloads.
  • Built modular REST API endpoints and optimized database query execution.
Node.jsTypeScriptBullMQRedisDockerREST APIs

Process

How I work

01

System Assessment

Understand current architecture, pain points, scale targets, and technical debt.

02

Architecture Design

Create system diagrams, data models, API contracts, and infrastructure blueprints.

03

Technical Leadership

Guide implementation with coding patterns, code reviews, and architectural decisions.

04

Production Excellence

Establish deployment pipelines, monitoring, alerting, and performance baselines.

FAQ

Frequently asked questions

Do you build the system or just design it?

Both. I've architected and shipped production systems like Planfluence, GeniSteps, DispatchLink, and Kookabe AI Suite. I design AND build.

What scale have your systems handled?

100k+ monthly background jobs with sub-85ms p95 API response times, sub-second audio transcription pipelines, and 24/7 uptime for enterprise customer-facing systems.

Can you audit an existing architecture?

Yes. I review database schemas, API designs, deployment pipelines, and infrastructure topology — then provide actionable recommendations for performance, scalability, and reliability.

How do you approach distributed systems?

Event-driven architecture with Kafka for cross-service communication, BullMQ for async job processing, Redis for caching/rate-limiting, and strict service boundaries with clear API contracts.

Need a system designed for scale?

From database architecture to deployment strategy — I design systems that scale gracefully under real production load.