Freelance DevOps Engineers in BoulderFreelance DevOps Engineers in Boulder
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Alexis Nieves's avatar
Alexis Nieves
Denver, USA
Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert
New to Contra
Senior DevOps/SRE | AWS, Kubernetes & Terraform Expert
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 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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Cover image for Implementing Additional Checks for AWS Security
Implementing Additional Checks for AWS Security
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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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DevOps Engineer(2)
Guadalupe Gallegos's avatar
Guadalupe Gallegos
Denver, USA
AI Automation & Security Engineer
New to Contra
AI Automation & Security Engineer
Cover image for 🟩 MSH OPS // DEV LOG — A Visual Transmission The
🟩 MSH OPS // DEV LOG — A Visual Transmission The Operator Division goes live. This isn’t a trailer — it’s a signal. A broadcast from the cockpit showing how we build, test, and evolve the MatrixSecHub architecture in real time. 🎥 Video: MSH OPS Dev Log 🧠 Focus: AI Engineering × Cyber Ops × System Design ⚙️ Objective: Demonstrate the tactical workflow behind the TTX engine and operator systems. Every frame is engineered — no filler, no fluff. Just pure transmission from the field. Subscribe on LinkedIn https://www.linkedin.com/build-relation/newsletter-follow?entityUrn=7435095198671519744 #MatrixSecHub #MSHOPS #AIEngineering #CyberOps #DevLog #OperatorDivision #TTX2050 #AureliusSignal
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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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Cover image for ✨ Meet AURELIUS — intelligence
✨ Meet AURELIUS — intelligence under authority. As AI systems become more capable, the central question is no longer simply: What can the system do? The more important question is: What is the system permitted to do—and can an operator understand why? AURELIUS is the governance interpreter inside the MSHOPS.NET (http://MSHOPS.NET) / Pearl OS ecosystem. 🧭 She is not an unrestricted autonomous agent. She does not replace human judgment. She does not create her own authority. Her role is to transform complex operational signals into clear, governed decisions. AURELIUS helps interpret: 🟡 Beacon governance boundaries 🔐 Agent permissions and execution scope 📊 Risk scores and approval requirements 📡 Runtime telemetry and evidence 🧠 Mission state and system uncertainty 🟢 Verified, sandboxed, or blocked actions The operating model is deliberate: BEACON defines the boundary. AURELIUS interprets the boundary. Agents operate within the boundary. The Operator retains authority. This cinematic concept introduces AURELIUS through the visual language of Pearl OS: 🤍 Pearl white ⚙️ Platinum and polished silver ✨ Champagne gold 🔵 Disciplined Pearl OS blue 🪟 Transparent system layers 📐 Calm technical precision The objective was not to create another fictional AI assistant. The objective was to visualize what governance-first intelligence should feel like: 👁️ Observable 🔎 Explainable 🛡️ Bounded 📁 Evidence-backed 🧑‍💻 Human-controlled Autonomous orchestration should never mean unrestricted authority. That principle is becoming foundational to everything we are building at MSHOPS.NET (http://MSHOPS.NET).
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Cover image for SIGNAL: MSH OPS DIVISION IS
SIGNAL: MSH OPS DIVISION IS NOW LIVE. A dedicated Operator‑Grade AI Security Division for organizations that can’t afford guesswork, noise, or “good enough” engineering. MSH OPS exists for one purpose: to integrate institutional‑grade AI systems into real infrastructure with zero friction and zero ambiguity. 📶 Who This Is For MSH OPS is built for organizations that need real AI capability, not experiments: Founders & CTOs who need AI systems that won’t break under scale Security teams who need adversarial testing, governance, and zero‑trust pipelines Engineering leaders who need multi‑model workflows that actually integrate Ops teams who need automation without losing control Agencies & consultancies who need a division‑grade backend to deliver AI to clients Enterprises who need clarity, architecture, and risk‑free deployment maps If your organization needs clarity, security, and operational AI, this Division is built for you. 📡 What I Do (Division‑Grade Capabilities) As Founder & Head of AI, I architect and deliver: AI Security Architecture Zero‑trust pipelines, GPU estate hardening, governance, adversarial testing. Multi‑Model Engineering Pipelines Claude, GPT‑5.1 Codex, DeepSeek, Qwen, Gemini, Perplexity — orchestrated, not improvised. Division‑Grade Client Integration Lineage‑driven agents, automated reporting, HITL‑on‑top S‑Layer design. Infrastructure Briefings Clear, cinematic, step‑by‑step deployment maps for leadership and engineering teams. Operator‑Grade Workflow Design Systems that feel like a frictionless, cinematic interface — not a legacy toolchain. This is not a brand. Not an agency. Not a freelancer service. It’s a Division — with doctrine, pipelines, agents, and operator workflows designed for scale. ⚡ Free Infrastructure Briefing Now Open If you want to understand: Where AI fits into your infrastructure What risks exist in your current pipelines What a division‑grade deployment looks like How to move from experimentation → institutional capability Reply BRIEFING or visit: www.mshops.net (http://www.mshops.net) 🫡📶📡 MSH OPS is online.
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DevOps Engineer(1)
John Samuelson's avatar
John Samuelson
Denver, USA
Transforming ideas into ML solutions & web apps
Transforming ideas into ML solutions & web apps
Cover image for Engineering Feedback Intelligence Platform
Engineering Feedback Intelligence Platform
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Cover image for Crypto Currency Exchange
Crypto Currency Exchange
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Cover image for Text Messaging Platform
Text Messaging Platform
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DevOps Engineer(1)
Cory Schmaltz's avatar
Cory Schmaltz
Denver, USA
Full Stack Engineer & AI System Architect & Network Engineer
Full Stack Engineer & AI System Architect & Network Engineer
Cover image for NLP pipeline for Federal Document Processing
NLP pipeline for Federal Document Processing
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Cover image for Obsidian Network Architect
Obsidian Network Architect
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Cover image for Logistics System for US Army
Logistics System for US Army
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Cover image for AI-Powered Automatic Marketing System
AI-Powered Automatic Marketing System
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DevOps Engineer(1)
Nicolas Vinson's avatar
Nicolas Vinson
Boulder, USA
👨‍💻
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Cover image for Active K9
Active K9
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Cover image for Games Assessment
Games Assessment
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Cover image for Serverless Reporting
Serverless Reporting
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DevOps Engineer(1)