You will get Gigs Hub AI Engine — Smart Freelancer Matching + Bid Ranking REST API
Project details
Your platform shouldn't need a human to decide which freelancer fits a project or which bid to accept. This AI engine handles both.
I deploy a standalone microservice that connects to your existing platform via API.
It does two things: when a client submits a project, the AI reads the description, extracts required skills — including implicit ones the client never wrote — and scores your entire freelancer pool, returning ranked matches with reasoning in under 10 seconds. When freelancers bid, it evaluates every bidder across skill match, proposal quality, track record, ratings, and price — returning a ranked table with per-bidder scores, strengths, concerns, and a hire recommendation.
Works with any backend. Django, Laravel, Node, Bubble — if it makes HTTP requests, it integrates in under 2 hours of developer time.
Delivery includes your API key, Postman collection, interactive Swagger UI at /docs, a developer integration guide, and a Loom walkthrough of your specific setup.
I deploy a standalone microservice that connects to your existing platform via API.
It does two things: when a client submits a project, the AI reads the description, extracts required skills — including implicit ones the client never wrote — and scores your entire freelancer pool, returning ranked matches with reasoning in under 10 seconds. When freelancers bid, it evaluates every bidder across skill match, proposal quality, track record, ratings, and price — returning a ranked table with per-bidder scores, strengths, concerns, and a hire recommendation.
Works with any backend. Django, Laravel, Node, Bubble — if it makes HTTP requests, it integrates in under 2 hours of developer time.
Delivery includes your API key, Postman collection, interactive Swagger UI at /docs, a developer integration guide, and a Loom walkthrough of your specific setup.
AI Development Type
Deep Learning, Model Tuning, Recommendation SystemAI Tools
SonnetAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$400
|
Standard
$1,200
|
Advanced
$2,800
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | |
Model Documentation | - | ||
Ontology | - | - | |
Source Code | - | - | |
Taxonomy | - | - |
Optional add-ons
You can add these on the next page.
Custom scoring weights
+$150
React: match display card
+$250
React: ranked bid table
+$250Frequently asked questions
About Fred
AI Systems Engineer | LLM Evaluation (RLHF) | Full-Stack Engineer
Nairobi, Kenya - 12:03 am local time
Most engineers stay on infrastructure.
Most annotators stay on task execution.
I’ve worked across both.
That means I don’t just design pipelines or deploy models in isolation—I understand how data is labeled, where quality breaks, and what actually improves model performance.
Result: systems built for real-world performance—not theory.
🛠 Software Engineering
I design and deploy full-stack systems built for real operations—not demos.
WeDemo Africa — AI + Human Talent Ecosystem
A production ecosystem I built to move users through:
Mindset → Skills → Monetization
This is a multi-layered operational system, not a content platform.
Core system design:
User progression: onboarding → learning → execution → earning
Hybrid AI + human workflows (mentorship, assessments, guidance)
Integrated platforms: learning, work, referrals, leadership
Gated progression: certification, assessments, controlled access
Operational layer:
Structured training pipelines
Dashboard-driven user tracking
Real-world outcome focus (not just completion)
✔ Live system handling real users and workflows
Mini Gigs Hub — Team Operations Dashboard
A production system for managing distributed teams:
Role-based access control
AM/PM shift reporting
Automated email alerts (cron jobs)
Admin analytics & performance tracking
Infrastructure & Systems
RustDesk relay & ID servers (VPS deployment)
Secure remote desktop environments
Browser isolation + proxy/session workflows
Core Stack
Next.js · React · TypeScript
PostgreSQL · Prisma · REST APIs
Vercel · Docker · VPS
Clerk · Role-based access
Cron jobs · Automation
JSON · CSV · JSONL pipelines
🤖 AI Systems Engineering
I design the operational layer between AI systems and the people training them.
That includes:
Annotation pipelines (RLHF, ranking, classification, etc.)
Task design + annotation guidelines
QA systems for consistent output at scale
I also build AI-powered APIs:
LLM integrations
Annotation workflow endpoints
Prediction-serving APIs
✔ Production-ready, documented, and integration-ready from day one
Training Systems
I’ve built a complete AI annotation training program:
5 structured modules
Covers text, image, audio, video + RLHF
Includes ethics, bias, and PII handling
Comes with:
Lesson scripts
Slides
Exercises
Assessments
✔ Ready for teams of 3–50
🧠 AI Evaluation
I’ve worked directly on:
Remotasks
Handshake
Mercor
So I understand:
How tasks are designed
Where they break in practice
What actually produces high-quality data
What I evaluate:
RLHF preference ranking
Prompt/response quality
Instruction-following accuracy
Hallucinations & factuality
Safety, bias, compliance
I also train evaluators in judgment—not just mechanics.
🎯 Who I Work With
AI companies building or refining LLM systems
Teams setting up annotation / RLHF pipelines
Organizations deploying training + workforce systems
If you need someone who understands:
systems + data + people + execution
—that’s where I come in.
🚀 Let’s Work
Send me a message with what you're building.
I’ll respond with a clear, technical breakdown of how I’d approach it—usually within a few hours.
Steps for completing your project
After purchasing the project, send requirements so Fred can start the project.
Delivery time starts when Fred receives requirements from you.
Fred works on your project following the steps below.
Revisions may occur after the delivery date.
Profile sync & pool setup
I configure your API key, sync your freelancer profiles to the engine pool, and confirm the /freelancers/pool endpoint returns your correct count and types.
Project matching live
I wire the project analysis signal to your backend and run a live test: you submit a real project, the AI returns ranked freelancer matches with scores and reasoning within 10 seconds.