I Build AI
Systems That
Actually Ship.
Production backends, intelligent pipelines, multi-tenant SaaS, cloud infrastructure, and modern frontends. Full stack. From Nigeria.
Built for Production.
Not for Demo.
I'm Chibuzor Authur Okafor, and I design and build production AI systems that organizations can genuinely rely on. What shapes every decision I make is a conviction I arrived at early and have not found reason to abandon: software that matters has to be trustworthy, not just impressive, and that quality is something you engineer deliberately, from the first architectural choice through to how the system behaves under real conditions two years after launch.
My path into software engineering was unconventional by any measure. I studied Soil Science at university, then spent several years working in hospitality before writing a line of production code. There was no formal computer science education and no roadmap to follow. What there was, consistently, was curiosity about how things work at a fundamental level and a willingness to stay with difficult problems until they gave way. I taught myself software engineering by building real things, reading extensively, and being honest about what I did not yet understand. The journey was neither fast nor straightforward, but every stage of it reinforced the same lesson: if you want to understand something deeply, build something with it.
Studying Soil Science shaped the way I think about complexity more than I recognized at the time. The discipline requires understanding how interconnected systems behave over long periods, how a shift in one variable propagates through a network of dependencies to produce effects that are not obvious from the surface, and how systems degrade in ways that rarely announce themselves dramatically. It develops patience with things that are difficult to control and a habit of tracing root causes rather than treating symptoms. When I think about software architecture today, those same instincts are present: where are the hidden dependencies, where could this fail quietly over time, and what will it cost to maintain this system in three years?
Today I design and deliver complete software systems, taking full ownership from initial architecture through development, deployment, and long-term operation. I enjoy owning the entire lifecycle because architecture only proves itself when the software is running in production and being used by real people. My work spans backend services, frontends, cloud infrastructure, databases, and the AI components that connect them into coherent products. I treat artificial intelligence not as a feature added at the end of a project but as an engineered component that must be designed, measured, and maintained with the same discipline as any other critical part of the system.
What I find most meaningful about this work is not the technical complexity but the outcome it produces. Software that holds up over time, that keeps functioning as the business around it evolves, and that remains comprehensible to the people who maintain it years after its launch is a quiet but significant kind of accomplishment. Technology will keep changing. Building software people can genuinely depend on is the part I hope never does.
Where I've Built
Real AI Systems
Contract engagements building production AI infrastructure for companies across industries. Every role was a full-stack responsibility.
Kuuka
AI Engineer, Contract
- ■Delivered AI-driven supplier intelligence automation and multi-tenant campaign infrastructure from scratch, enabling multiple client organizations to operate with strict data isolation on a single shared platform
- ■Secured 50+ production endpoints with RBAC-enforced tenant isolation and full CI/CD test coverage, enabling confident deployment across a multi-tenant environment
- ■Architected GDPR-compliant audit logging that shipped to production without post-deployment compliance remediation
Possibl
AI Engineer, Contract
- ■Designed a multi-agent conversational AI system with stateful memory using LangChain and LangGraph, enabling sessions that maintain full context across extended exchanges
- ■Built a natural language to SQL analytics pipeline over PostgreSQL, allowing non-technical users to query structured data in plain English without writing a single query
- ■Implemented automated memory summarization and human-in-the-loop decision routing, keeping AI responses within defined boundaries under real production conditions
Zenovo AI
Applied AI Engineer, Contract
- ■Built a RAG-powered proposal generation platform on PostgreSQL and pgvector, enabling teams to produce intelligent, context-aware RFQ and RFP responses automatically
- ■Reduced proposal preparation time by 70% through multi-agent LLM orchestration, turning a multi-day manual process into an automated workflow
- ■Engineered async processing infrastructure with Redis and Celery, decoupling generation workloads from request latency for reliable production throughput
Independent Clients
AI Engineer and ML Consultant
- ■Delivered RAG knowledge retrieval systems using FAISS, enabling enterprise clients to surface accurate information from large internal knowledge bases through semantic search
- ■Built Whisper-based transcription pipelines on Linux VPS, converting audio content to searchable structured text and eliminating the need for manual transcription
- ■Created natural language data exploration tools for Excel datasets and document embedding pipelines, allowing non-technical stakeholders to query and analyze structured data in plain English
Shipped Systems
Production applications built end-to-end. Click any card for the full engineering breakdown.
LearnVault
LearnVault
Production multi-tenant SaaS platform built end-to-end across eight integrated subsystems: custom auth with database-level tenant isolation, live classrooms via LiveKit with speaker diarization, AI-generated session summaries and quizzes, RAG-powered tutoring chatbot with pgvector, credit billing, async job processing with Asynq, and a unified PostgreSQL schema architected to support unlimited organizations from a single deployment.
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MailCheckr
MailCheckr
Self-hosted distributed email aggregation system: direct IMAP sync engine monitoring multiple accounts simultaneously with zero credential exposure to third parties, multilingual financial classification, real-time Telegram alerts on critical transactions, resilient async processing with Celery and Redis, a PostgreSQL storage layer, and a full-stack Next.js dashboard. Deployed on VPS behind Nginx.
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MailForge
MailForge
Native Windows desktop application for managing email campaigns across unlimited rotating SMTP accounts, with no per-email fees and no third-party credential exposure. Features an async send engine with pause/resume/cancel mid-campaign, self-healing circuit breakers per SMTP account, DKIM signing, bounce/complaint automation via IMAP/POP3, local open/click tracking, and full campaign history in a portable encrypted SQLite project file.
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GPI Invent
GPI Invent
Offline-first inventory management and POS platform for a live Nigerian electrical supply business. Three client surfaces (web admin, web POS, native iOS/Android) share one FastAPI backend and one sync contract: every sale commits locally in under two seconds and syncs opportunistically without blocking on the network. Conflicts surface to admins for explicit resolution rather than auto-resolving. Ships a multi-unit inventory model, a live financial portfolio dashboard, scheduled email reporting, encrypted nightly backups to the business's own Google Drive, and Gia, a read-only AI assistant constrained to pre-validated query templates so it can never generate or execute free-form SQL against production data.
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InfiniMail
InfiniMail
Full-stack AI email generation platform that produces unique, personalized content for every recipient in a campaign, eliminating the manual bottleneck of writing individual emails at volume. Features custom campaign management, personalization token support, OpenAI integration, and a Next.js dashboard for previewing and launching high-volume outreach at scale.
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Zorai
Zorai
Complete AI content operations system that eliminates manual content creation across four platforms simultaneously: knowledge ingestion with dual-mode retrieval (vector search combined with graph-based reasoning), LLM-generated posts tailored to the format and audience of each platform, human review before any publish, and Celery-based scheduling for fully automated distribution to X, LinkedIn, Substack, and Reddit.
View DetailsEngineering
Expertise
A full-spectrum engineering skill set. From intelligent AI systems to cloud infrastructure, every layer owned end to end.
AI & Intelligent Systems
I design and integrate AI capabilities into production software at the systems level: RAG pipelines backed by pgvector, multi-agent orchestration with LangGraph, LLM integration via OpenAI and Anthropic APIs, and intelligent automation with observable evaluation loops. AI becomes a reliable, scalable component of a larger engineered system, not a standalone experiment.
Backend Engineering
Scalable backend systems built with FastAPI and Go. RESTful APIs, JWT/OAuth2 authentication, async task queues with Celery and Redis, WebSocket connections, and everything from data modeling to API versioning and rate limiting.
Frontend Engineering
Production frontends with Next.js and React 19, TypeScript throughout, Tailwind CSS for styling, and Framer Motion for purposeful animation. Component architecture, responsive design, and performance optimization built in from the start.
System Architecture
Multi-tenant SaaS platforms, distributed systems, and event-driven architectures designed for real load. I design clean API boundaries, implement proper tenant isolation, model databases to grow, and make deliberate tradeoffs between consistency, availability, and complexity. Systems that ship and then stay up.
Databases
PostgreSQL as the primary data store with pgvector for semantic search, Redis for caching and queues, and Supabase for rapid prototyping. Query optimization, proper indexing, migration management, and vector search at production scale.
Cloud & Infrastructure
Production deployments on AWS, GCP, and Vercel, configured properly. VPS environments with Nginx reverse proxies, Docker containerization, CI/CD pipelines, environment management, and monitoring. Infrastructure built to stay up under real traffic.
Dev Tooling
Git-based workflows, shell scripting, Docker Compose for local parity, and a testing mindset from unit to integration. Consistent, reproducible environments from local development to production.
What I Build
End-to-end engineering capabilities, from system design to production deployment.
AI Systems Integration
I design and integrate AI capabilities into real production software: RAG pipelines, multi-agent orchestration with LangGraph, vector search with pgvector, and LLM-powered features via OpenAI and Anthropic APIs. AI becomes a reliable, scalable component of a larger system, not a standalone experiment.
Full-Stack Application Development
End-to-end product engineering from first commit to live URL. I build backends with FastAPI or Go, frontends with Next.js and React, wire up databases, authentication, and background workers, then ship to production. You get a complete, working product, not a collection of disconnected parts.
Backend Engineering & API Design
Scalable, well-structured backend systems built with FastAPI, Python, and Go. I design RESTful APIs, implement JWT and OAuth2 authentication, set up async task queues with Celery and Redis, configure WebSocket connections, and handle everything from data modeling to API versioning.
SaaS & Multi-Tenant Architecture
I architect and build SaaS platforms with proper multi-tenancy from the ground up: tenant isolation, role-based access control, per-organization data boundaries, subscription and credit billing, and scalable PostgreSQL schemas designed to grow with your user base.
Cloud Infrastructure & Deployment
Production deployment on AWS, GCP, and Vercel, configured properly. I set up VPS environments with Nginx reverse proxies, Docker containerization, environment management, CI/CD pipelines, and monitoring. Your application runs on infrastructure that is built to stay up.
Intelligent Automation
I build automation systems that do real work: email classification and alerting engines, AI-powered content scheduling and publishing pipelines, data sync workers, and multi-platform integrations. Built on Celery, Redis, and event-driven patterns for reliability at scale.
Let's Work
Together
If you are building an AI-powered product, integrating intelligent systems into an existing platform, or looking for an engineer who can own the work from architecture through deployment and long-term operation, I want to hear what you are working on.
Currently open to
- Full-time and contract AI engineering roles at product companies and startups
- Technical architecture consulting and system design engagements
- Founding engineer roles where AI is a core part of the product
- Long-term technical partnerships on SaaS platforms, AI systems, and backend infrastructure
chibuzorauthur@gmail.com
+2349036546571