Freelance Systems EngineersFreelance Systems Engineers
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Kokorick AI's avatar
Kokorick AI
Houston, USA
AI Agents | LLMs, Computer Vision & Full-Stack Dev
69
Followers
AI Agents | LLMs, Computer Vision & Full-Stack Dev
Cover image for 🚨 Turning CCTV Into a
🚨 Turning CCTV Into a Crime-Prevention AI System We helped build Aegis FaceGuard, an AI-powered security platform designed to prevent armed robberies before they happen—not after. In high-risk regions like Colombia, traditional CCTV only records. This system predicts, detects, and reacts — instantly. Here’s what it catches in real time: 🪖 Helmet detection 🔫 Gun detection 🎭 Mask detection 👤 Suspicious posture ⚠️ Threat movement patterns When danger appears, the system automatically: Triggers alarms Alerts police Notifies the owner Saves evidence — all within milliseconds This is security before the crime, not after. If you're building AI for public safety, retail protection, or real-world automation, I’d love to connect. 🔥 #AI #ComputerVision #SecurityTech
15
244
Cover image for AI-Powered Code Editor for Modern Developers
AI-Powered Code Editor for Modern Developers
0
5
Cover image for WorkMind AI: Unified AI Workplace Platform
WorkMind AI: Unified AI Workplace Platform
0
1
Cover image for Aegis FaceGuard: AI-Powered Crime Prevention
Aegis FaceGuard: AI-Powered Crime Prevention
0
4
Systems Engineer(1)
Usama Idrees's avatar
Usama Idrees
Islamabad, Pakistan
Cloud · DevOps · AI/ML · Email · Marketing Expert
$5k+
Earned
12x
Hired
5.0
Rating
32
Followers
Cloud · DevOps · AI/ML · Email · Marketing Expert
Cover image for Legacy System Scalable and Secure Modernization
Legacy System Scalable and Secure Modernization
2
23
Cover image for Implementing DevOps Practices; Enterprise-Level Transformatios
Implementing DevOps Practices; Enterprise-Level Transformatios
2
62
Cover image for Maximize Data Protection & Availability with Vetted Veeam Expert
Maximize Data Protection & Availability with Vetted Veeam Expert
0
21
Cover image for Citrix DaaS Implementation & Optimization Services
Citrix DaaS Implementation & Optimization Services
0
16
Systems Engineer(4)
Khondoker Ali Asgor Pavel's avatar
Khondoker Ali Asgor Pavel
Dhaka, Bangladesh
Experienced IT Specialist & WordPress Developer
$5k+
Earned
2x
Hired
5.0
Rating
18
Followers
Experienced IT Specialist & WordPress Developer
Cover image for WatchSaver LLC Server Management
WatchSaver LLC Server Management
0
8
Cover image for SEO for customviallabels.com & vialpackaging.com
SEO for customviallabels.com & vialpackaging.com
0
7
Cover image for 2r3ml Website SEO
2r3ml Website SEO
0
6
Cover image for 2r3ml.com Website Development
2r3ml.com Website Development
0
8
Systems Engineer(1)
El Mehdi El Wafi's avatar
El Mehdi El Wafi
proMorocco
Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform
$5k+
Earned
1x
Hired
9
Followers
Linux VPS SysAdmin AWS & Oracle Cloud Architect | Terraform
Cover image for Enterprise VM Template for Proxmox VPS
Enterprise VM Template for Proxmox VPS
0
5
Cover image for DevOps/Cloud & DataOps maintainer
$9.1K+ earned
DevOps/Cloud & DataOps maintainer
3
24
Cover image for 92% Cost OFF of EC2 Airflow/DBT Workers using Lambda
92% Cost OFF of EC2 Airflow/DBT Workers using Lambda
1
11
Cover image for 🛠️ DevOps Tips 🛠️ If
🛠️ DevOps Tips 🛠️ If your GitHub workflows or jobs are failing due to jobs concurrently accessing and/or modifying shared resources, You can control execution at the workflow or job levels using the concurrency. Jobs/Workflows in the same are prohibited from running at the same time, and you can cancel any jobs/workflow if you trigger another one. Groups let you define compartments in which you don't want concurrent workflows running. They can be organized however you like. I find that using them in separate environments is a great practice. Cancel-in-progress: specifies if the old running job/workflow needs to shut down. Read more in docs: https://docs.github.com/en/actions/how-tos/write-workflows/choose-when-workflows-run/control-workflow-concurrency
2
3
479
Systems Engineer(2)
Rusty Williamson's avatar
Rusty Williamson
proLittle Rock, USA
Business Automation Architect | AI Agents & Integrations
New to Contra
Business Automation Architect | AI Agents & Integrations
Cover image for Building an AI-powered affiliate
Building an AI-powered affiliate publishing engine from the ground up. I am currently working on a full automation system for NodeRidge, designed to turn product and software research into structured, review-ready WordPress content with human approval built into the workflow. The goal is simple: remove the repetitive work from affiliate publishing while keeping quality control, visibility, and editorial review in place. This build connects: 🗄️Airtable as the central operations hub ⚡Make.com (http://Make.com) for workflow automation 🧠OpenAI for content generation 📝WordPress for draft publishing 📊Google Search Console for rank and performance tracking What I like most about this project is that it is not just "AI writing content." It is a real backend publishing engine with structured data, review gates, sync monitoring, and operational visibility. The system is already handling prompt runs, WordPress draft syncing, affiliate link registry logic, and QA tracking. My next focus is scaling the Airtable architecture to handle higher volume. This is the kind of build I enjoy most: connecting messy manual workflows into a clean system that can actually scale. What are the biggest operational bottlenecks you're currently dealing with in your own workflows?
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92
Cover image for Stop building science experiments and
Stop building science experiments and start building scalable, autonomous operational systems. I just published a detailed look under the hood at my most sophisticated AI integration yet: The Hierarchical Multi-Agent Business System. Most founders use ChatGPT to write emails. I build AI agent teams to source leads, design technical architectures, manage client-facing dashboards, and automate complex content pipelines—all with zero human input for everything except final approval. Take a look at the attached infographic to see how this recursive, self-optimizing architecture functions. Here is the operational impact of what I build: Departmental Specialization: I don't use one "smart" agent. I build a crew of highly specialized agents (Lead Gen, Content, DevOps) overseen by a Manager Agent to ensure quality and prevent hallucinations. The Human-in-the-Loop Safeguard: The entire workflow is designed to be fully autonomous until final quality control and strategic approval. You get the scale of AI with the certainty of a human final check. Tool Agnosticism: My builds are logic-first. We use the best tool for the task, whether that's CrewAI, LangGraph, Supabase, Cloudflare, OpenAI, or a custom API. If you are a founder or operator ready to turn your standard business workflows into high-performance, autonomous engines, check out the full case study. https://contra.com/s/2rFLgNAU-custom-ai-agent-for-your-business-workflow
1
96
Cover image for Architected an autonomous AI pipeline
Architected an autonomous AI pipeline that ingests product links, conducts SEO research, and generates publish-ready affiliate reviews with zero manual writing.
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36
Cover image for Building Tether — From Noisy
Building Tether — From Noisy Telemetry to Deterministic Operations Role: Lead Architect & Full-Stack Developer Tech Stack: React, Cloudflare (Pages, Workers, R2, Zero Trust), Google Cloud Platform (Cloud Run, Cloud Storage), Python, FastAPI, Scikit-Learn. The Challenge: The Hospitality Data Gap Modern hospitality operators are drowning in data but starving for actionable intelligence. A restaurant's two most critical systems—the Point of Sale (revenue) and the scheduling platform (labor)—operate in complete isolation. Because these systems do not dynamically communicate, managers are forced to make high-stakes labor cuts on the fly based on delayed reporting and gut feeling. This disconnect results in thousands of dollars of weekly margin bleed. The challenge was clear: build a system that bridges these fragmented APIs, normalizes the data, and provides real-time operational certainty. The Solution & The Product Pivot I engineered Tether to be an AI-native operational layer for restaurant management. However, the true breakthrough of this project wasn't just technical—it was architectural. Initially, I designed Tether as a "Live Data Prediction Tool" that used active telemetry to drive real-time floor decisions. Through testing and auditing the data streams, I identified a critical UX flaw: live data is inherently noisy and reactive. To solve this, I executed a complete priority inversion, refactoring the application state to a "Schedule-First" philosophy. Instead of chasing live data, Tether now ingests historical data to generate a deterministic, highly optimized 14-day schedule baseline. The machine learning models were strategically demoted from "decision makers" to "real-time guardrails." Once the floor opens, Tether acts as a safety net, validating execution against the baseline and alerting managers to profit leaks before they compound. Technical Execution: A Masterclass in Edge ML To ensure security, scale, and sub-100ms latency, I architected Tether as a zero-backend Single Page Application (SPA) driven by serverless microservices. Edge Infrastructure & Security: The frontend is deployed via Cloudflare Pages and secured behind a Cloudflare Zero Trust perimeter, requiring One-Time PIN (OTP) authentication for operator access. Data Normalization: I developed Cloudflare Worker proxies to securely handle OAuth handshakes, ingest data from POS systems (Square, Toast) and labor platforms (7shifts), and normalize the varied streams into a unified, sanitized client schema. Autonomous ML Pipeline: I engineered a fully autonomous, serverless retraining loop hosted on Google Cloud Run. Every Tuesday at 3:00 AM UTC, the pipeline wakes up, pulls historical telemetry from Cloudflare R2, and retrains the primary Approval and Labor-to-Sales (LTS) models (using Ridge and Logistic Regression). Strict Data Contracts: The ML pipeline strictly enforces a 63-feature data contract. It validates baseline accuracy and ensures zero NaNs before allowing any model to pass into production, guaranteeing operational stability. Highly Optimized Model Distribution: Fresh model weights are served to the browser via a Dockerized FastAPI microservice (kept aggressively lean at ~500MB) and distributed globally through Google Cloud Storage (GCS). The Business Impact Tether replaces the anxiety of restaurant management with mathematical certainty. By automating the schedule generation and monitoring real-time Labor-to-Sales (LTS) velocity, Tether catches margin bleed live—such as a sudden drop in patio sales pace due to weather. It translates complex ML predictions into simple, actionable alerts (e.g., "Trim one support role. Protects $190 margin."). The result is protected daily profit margins, guaranteed labor compliance, and management teams empowered to run their floors with absolute confidence.
0
54
Systems Engineer(4)
Safa Souli's avatar
Safa Souli
Tunis, Tunisia
UI/UX Designer & Developer
5.0
Rating
10
Followers
UI/UX Designer & Developer
Cover image for Curatech – Website Design & Development
Curatech – Website Design & Development
1
3
Cover image for SecureIVAI
SecureIVAI
0
0
Cover image for Genesis Robotics: Transforming Productivity
Genesis Robotics: Transforming Productivity
1
3
Cover image for Qatar University - Redesgin
Qatar University - Redesgin
1
240
Systems Engineer(3)
Otavio Menezes's avatar
Otavio Menezes
Louisville, USA
Custom Automation to Scale Your Business Fast
5.0
Rating
8
Followers
Custom Automation to Scale Your Business Fast
Cover image for AI-Personalized Cold Email System
AI-Personalized Cold Email System
0
47
Cover image for Custom AI Chatbot for Customer Support
Custom AI Chatbot for Customer Support
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33
Cover image for 👋 Hey there, I automate things...
👋 Hey there, I automate things...
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42
Cover image for AI Generated Proposal System
AI Generated Proposal System
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27
Systems Engineer(2)