Freelancers using Python in Denver

Freelancers using Python in DenverFreelancers using Python in Denver

Projects People

Guadalupe Gallegos

AI Automation & Security Engineer

New to Contra

AI Automation & Security Engineer

Cover image for Ghost Layer Studio bridges cinematic design and

Ghost Layer Studio bridges cinematic design and system architecture through the MatrixSecHub Core Map and the Melius Canvas. The Core Map defines mission flow, escalation protocols, and ethical oversight — the operator’s containment grid. The Melius Canvas captures the Ghost Layer workflow in its final state: agent‑verified, repo‑aligned, and sealed for archival. External Repo: Ghost Layer Studio on GitHub (https://github.com/matrixsechub/gH0ST-LaY3r) Melius Canvas: Ghost Layer Studio Submission (https://app.melius.com/projects/400f5d77-6d95-497f-b097-9640ff81fe37/canvas/40c7847a-22a8-4a5e-851b-94336665c110) Signal Detected // System Offline // Archive Ready Tools Used: Image Tool, Text Tool, Agent Tool (Gemini 3.1 Pro), Canvas Tool

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Cover image for AI Hiring Workflow & Candidate

AI Hiring Workflow & Candidate Automation System I built an end‑to‑end AI‑powered hiring workflow that automates the entire candidate lifecycle — from screening to onboarding. This system replaces manual review, inconsistent communication, and slow decision‑making with a clean, structured, and fully automated pipeline. What it does: • Screens resumes and applications with role‑specific criteria • Extracts skills, experience, and red flags automatically • Generates structured candidate summaries for fast review • Automates candidate communication and follow‑ups • Schedules interviews and routes candidates to the right stage • Delivers onboarding steps and training materials automatically Why it matters: Hiring is one of the most chaotic operational areas for most teams. This system turns it into a predictable, auditable workflow that saves hours per week and improves candidate experience. Tech used: AI models, embeddings, workflow automation (n8n), async operations, secure data handling, custom scoring logic. Outcome: A fast, fair, and scalable hiring pipeline that reduces time‑to‑hire, improves clarity, and eliminates repetitive manual work.

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Cover image for ⚙️ Intelligent Business Automation System
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⚙️ Intelligent Business Automation System I build intelligent automation systems that eliminate manual work and keep operations running clean, fast, and predictable. This system combines AI reasoning, workflow automation, and secure integrations to handle the tasks teams shouldn’t be doing manually. What it does: • Automates repetitive workflows end‑to‑end • Connects tools like n8n, Notion, APIs, and internal systems • Handles logic, routing, and decision‑making • Reduces operational drag and human error • Runs asynchronously and scales with workload Why it matters: Most businesses lose hours every week to manual tasks. This system replaces that friction with a reliable automation layer that executes instantly and consistently.

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Cover image for 👤 The most important role

👤 The most important role inside Pearl OS is not an AI agent. It is the Operator. As AI systems become more capable, many platforms are designed around removing the human from the loop. That is not what we are building at MSHOPS.NET (http://MSHOPS.NET) Pearl OS is designed to automate routine coordination while preserving human authority at every consequential boundary. The system may: 🧠 Research and analyze 🔄 Coordinate agents and workflows 🧪 Test and validate 📡 Monitor telemetry 📦 Prepare releases 🧾 Assemble evidence But preparation is not permission. The Operator retains authority over: 🧭 Mission direction 🔐 Scope and permissions ⚠️ Risk acceptance 🚀 Production releases ↩️ Rollbacks 🛑 Safe-mode and termination decisions The operating model is deliberate: BEACON defines the boundary. AURELIUS interprets the boundary. Agents operate within the boundary. Telemetry records the outcome. The Operator makes the decision. The Operator should not be trapped inside every task. The Operator should be present at every consequential boundary. That means being able to answer five questions at any moment: What is happening? Why is it happening? What authority is being used? What evidence supports continuation? What requires my decision? This cinematic chapter explores the human role inside a governed multi-agent operating system. The system may move quickly. Authority should never move silently. 🎬 Inside Pearl OS — Part 2: The Operator Remains the Authority Next: HSX — the system that trains, protects, and pressure-tests Pearl OS.

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Mohammed Alsamdani

Data Scientist | Renewable Energy Analyst | Prompt Engineer.

Data Scientist | Renewable Energy Analyst | Prompt Engineer.

Cover image for Analyzing Customer Energy Consumption Patterns

Analyzing Customer Energy Consumption Patterns

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Cover image for Power Systems Optimization using Data Science

Power Systems Optimization using Data Science

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Cover image for Renewable Energy Data Analysis

Renewable Energy Data Analysis

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Developing custom websites and scalable software.

10: Followers

Developing custom websites and scalable software.

Cover image for HCTC - Inventory Management Solution

HCTC - Inventory Management Solution

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Cover image for Cavenwell Revolutionizing AI-Driven Efficiency Across Industries

Cavenwell Revolutionizing AI-Driven Efficiency Across Industries

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Tech-Agnostic MVP Creator

Tech-Agnostic MVP Creator

Cover image for Leadership Analytics/AI - for ghSMART

Leadership Analytics/AI - for ghSMART

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Cover image for Essential Startup Bytes

Essential Startup Bytes

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Cover image for Fakey - Media Literacy App

Fakey - Media Literacy App

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Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert

New to Contra

Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert

Cover image for Designed and developed a Command

Designed and developed a Command Line Interface (CLI) security tool that leverages the Google Gemini API (gemini-2.5-flash) to perform advanced, AI-driven vulnerability analysis on Python codebases. This tool acts as an automated security engineer, scanning source code to detect critical flaws before they reach production. The scanner parses Python files and evaluates them against a detailed security prompt to identify vulnerabilities such as SQL injection, hardcoded secrets (API keys and database passwords), weak cryptography (e.g., MD5 hashing), insecure deserialization, and improper input validation. It outputs a structured, color-coded terminal report assigning severity ratings (🔴 High, 🟠 Medium, 🟢 Low) to each finding. For every detected issue, the tool provides the vulnerability type, the exact line number, a concise explanation of the risk and potential impact, and actionable remediation advice with secure code fixes. The architecture is built on a lightweight Python foundation using a virtual environment for dependency isolation. It utilizes the google-generativeai SDK for model interaction and python-dotenv for secure environment variable management, ensuring API keys are never hardcoded. This project demonstrates the ability to integrate cutting-edge Large Language Models (LLMs) into traditional DevSecOps workflows, providing enterprise-grade security scanning capabilities directly within the developer's terminal.

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Cover image for Comparing AWS ECS and AWS EKS for Container Orchestration

Comparing AWS ECS and AWS EKS for Container Orchestration

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Cover image for Implementing Additional Checks for AWS Security

Implementing Additional Checks for AWS Security

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Cover image for Architected and implemented a production-grade,

Architected and implemented a production-grade, fully automated DevOps platform on AWS, covering the entire software delivery lifecycle from code commit to live deployment. The platform integrates eight tightly coupled layers: a developer workflow built on Git, Docker Compose, and pull requests; a GitHub Actions CI/CD pipeline with dedicated workflows for continuous integration, deployment, Terraform provisioning, security scanning, and semantic releases; and a multi-tool security gate enforcing SAST, SCA, IaC scanning, container analysis, OWASP ZAP, and CodeQL checks before any artifact progresses. Infrastructure is provisioned as code using Terraform, deploying a multi-AZ VPC, an EKS cluster, ECR repositories, RDS MySQL, an ALB with WAF, and IAM OIDC integration on AWS. Applications are packaged with Helm Umbrella Charts and deployed to Kubernetes with Horizontal Pod Autoscaling (2–8 replicas) and RBAC-enforced NetworkPolicies. GitOps state management is handled by ArgoCD, providing continuous reconciliation, drift detection, auto-sync, and self-healing across all application sets. Observability is delivered through a dual-stack setup: a metrics pipeline (Prometheus → Grafana → Alertmanager → Slack) and a log aggregation pipeline (Filebeat → Logstash → Elasticsearch → Kibana). Releases are fully automated using Semantic Release with Conventional Commits, producing versioned GitHub Releases and Docker image tags without manual intervention.

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Alexander Chester

10 years in Business Consulting & Creative Leadership

10 years in Business Consulting & Creative Leadership

Cover image for Founder, Ops Lab (Operations & business design agency)

Founder, Ops Lab (Operations & business design agency)

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Cover image for Starlink Operations Leader at SpaceX

Starlink Operations Leader at SpaceX

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Cover image for Head of Operations for organic CPG startups (1M -> 20M in 2yr)

Head of Operations for organic CPG startups (1M -> 20M in 2yr)

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Pioneering AI/Data/Software engineering lead— Builder/solver

Pioneering AI/Data/Software engineering lead— Builder/solver

Cover image for Toolio platform for Private AI/Large Language Model Agents

Toolio platform for Private AI/Large Language Model Agents

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Cover image for 10minclimate.com

10minclimate.com

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Cover image for BiblioGraph | EBSCO

BiblioGraph | EBSCO

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Cover image for Inclusive Guide

Inclusive Guide

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Full Stack Web Developer

Full Stack Web Developer

Cover image for Goodnight Hospitality

Goodnight Hospitality

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Cover image for Drunk Elephant Smoothie Finder

Drunk Elephant Smoothie Finder

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Cover image for Vintage for Y'all

Vintage for Y'all

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