DevOps Engineer to Set Up Private AI Agent Server (One-Time Setup & Training Only)

@Sohail748

Hi there, I'm offering a 25% discount on this project. As a DevOps engineer specializing in AI infrastructure, I will set up a complete private AI agent server for your organization—handling the one‑time installation, configuration, and training so you can run your own AI models securely and independently. I'll start by understanding your AI requirements, preferred models, and infrastructure preferences. I will then deliver a complete private AI server setup including server provisioning and OS configuration (your hardware or cloud), installation of AI frameworks and dependencies (Ollama, Llama, etc.), downloading and configuring your chosen models, setting up API endpoints for integration, implementing security measures and access controls, configuring monitoring and logging, optimizing performance for your hardware, creating user documentation and training materials, and providing a training session for your team on managing and using the system. You'll receive a fully functional private AI agent server running on your infrastructure, along with comprehensive documentation and team training—all with a one‑time setup fee and no ongoing obligations. Let's bring AI in‑house with a private, secure server. Best regards, Sohail

@mayankinnovative

Hi, As per my understanding: You need a secure, private AI automation system deployed on your Ubuntu 24.04 VPS (2 CPU, 8GB RAM) using Docker only—no Kubernetes. The goal is a clean V1 foundation: modular Python agent framework, one orchestrator agent delegating to at least one sub-agent, LLM API integration (Claude or similar), lightweight web UI for natural language input, structured JSON outputs, logging, scheduled tasks, and full documentation/training so you can operate it independently. Implementation approach: I will design a Docker-based modular architecture: Nginx reverse proxy, FastAPI backend, agent core (task router + worker agents), Redis (queue/state), and PostgreSQL or SQLite (metadata/logs). LLM access via secure API layer with env-based secrets. Agents will follow a structured task schema with JSON contracts and validation. Logging via structured logs + rotation; scheduling via Celery beat or APScheduler. UI: minimal React or Jinja interface. Deliverables include docker-compose setup, security hardening (UFW, fail2ban, SSH hardening), documentation, and live walkthrough. A few quick questions: Preferred LLM provider? Do you require persistent vector storage? Expected task concurrency level? Timeline expectation for Phase 1?

@cindyviorinar

Hi, I’ve read your requirements and I’m confident I can set up a secure, private AI agent server on your Ubuntu 24.04 VPS and train you to operate it. I specialize in clean, modular ASP.NET Core/C# and Python backends and have strong hands-on DevOps experience with Docker-based deployments, secure Linux hardening, and API integrations. For this Phase 1 I will harden the server, install Docker, design a modular Python agent framework with one main agent delegating structured tasks to sub-agents, integrate an LLM API such as Claude, build a lightweight web UI for natural-language submissions, implement a structured JSON output layer, logging, and simple scheduler, and deliver clear documentation and a knowledge-transfer session. I favor clean architecture and maintainable code: the agent logic will be decoupled into services, communication via well-defined JSON schemas, and images kept minimal and reproducible. I will avoid Kubernetes or browser automation and will not set up any auto-push to production. Next step: I’ll share a concise high-level architecture diagram and a Phase 1 work breakdown so you can confirm scope and we can schedule the kickoff. Can you confirm which LLM provider you prefer (Anthropic Claude or another API), and whether you want the web UI accessible over the internet or restricted to your local network/VPN? Best regards, Cindy Viorina

@yeswanthmedida19

Hello, This is a clean V1 foundation project, and I understand the focus is modularity, security, and knowledge transfer — not overengineering. Proposed Architecture • Hardened Ubuntu 24.04 (UFW, SSH hardening, fail2ban) • Docker + Docker Compose (no Kubernetes) • Python-based agent framework using FastAPI • Main Orchestrator Agent → delegates structured tasks to sub-agent via internal message layer • LLM integration via Claude API (abstracted service layer) • Structured JSON response schema enforced at agent boundary • PostgreSQL or lightweight SQLite (based on need) • Logging via structured logs + rotating files • Simple web UI (FastAPI + minimal frontend) • Scheduled tasks using APScheduler • Clear container separation (app, db, proxy if needed) Everything will be modular so new agents can be added without rewriting the core. Estimated Effort ~25 hours Timeline: 7–10 days Fixed Phase 1 price: $200 I have built internal AI automation tools using Docker + Python + LLM APIs with clean agent orchestration layers and structured output enforcement. I focus on maintainable foundations, not experimental chaos. Happy to move quickly once aligned.

@servicedigi

We have 4+ years of backend and infrastructure experience (Python/FastAPI, Node.js), and we currently build AI systems including RAG pipelines, structured LLM outputs, and modular agent-style services. We focus on clean architecture, Dockerized deployments, and secure VPS setups. Proposed High-Level Architecture Infrastructure Ubuntu 24.04 hardened (UFW, fail2ban, SSH key-only, non-root user) Docker + Docker Compose (no Kubernetes) Nginx reverse proxy (HTTPS via Let’s Encrypt) Isolated Docker network Application Layer Python (FastAPI) core service Agent framework: Main Orchestrator Agent Sub-agent(s) with clearly defined task contracts LLM integration (Claude or similar via API wrapper module) Strict JSON schema output validation layer Structured logging (file + rotating logs) Interface Lightweight web UI (simple React or HTMX/Jinja templates) Task submission endpoint Status + logs viewer Basic scheduler (APScheduler or Celery-lite without broker complexity) Fully modular, extendable, and production-safe (no auto-push workflows). • Built private RAG-based AI pipelines with structured outputs • Designed Dockerized FastAPI microservices for automation systems • Deployed secure VPS-based AI tooling with logging + API orchestration Deliverables include full documentation, architecture diagram, and live walkthrough training so you can confidently operate and extend the system yourself. Ready to begin immediately once scope is confirmed.

@jonhw5

Hello, thanks for posting this project. I have reviewed your requirements and believe I can deliver a secure, maintainable, modular automation system tailored to your needs. My approach involves: - Secure server hardening and Docker-based service isolation - A Python-based modular agent system with a main agent delegating to sub-agents - LLM (Claude or similar) integration via API, with structured logging and scheduled task execution - Lightweight web interface for natural language command input and structured JSON output - Comprehensive documentation and live knowledge transfer I estimate Phase 1 will require 35–40 hours, with a proposed fixed price of $2,000 and a 2–3 week timeline from alignment. In my recent projects, I have built private LLM-powered automation platforms, API-driven AI agent frameworks, and Dockerized, modular backend infrastructures for secure self-hosted operations. Could you share a bit more about your expected task types or agent workflows, to ensure the architecture fully supports your requirements? Looking forward to your response.

@danielboggs

Hi there, I read your requirements and can deliver a secure, modular V1 agent platform on your Ubuntu 24.04 VPS. I’ll provision a hardened Docker-only environment, set up systemd-managed containers, and build a Python-based agent framework (main agent + delegating sub-agent pattern) using FastAPI for a lightweight web UI, a structured JSON output layer, LLM integration via API (Claude or chosen provider), simple scheduler (APScheduler), and central logging (file + optional JSON logs). I’ll provide clear docs, runbooks, and a live knowledge-transfer session so you can extend it yourself. High-level architecture: Docker host -> nginx reverse proxy + TLS -> FastAPI agent service (main + sub-agents) -> LLM API connector -> storage/logs. Estimated hours: 18-25 hrs. Phase 1 fixed price: $6. Estimated timeline: 3-5 days, focused on a clean, maintainable V1. Which LLM provider do you prefer (Claude or another) and do you already have API keys available? Best regards, Daniel

@imahad0

Drawing on my extensive expertise in DevOps and Docker, I am the ideal candidate to set up your private AI agent server. Being well-versed in automation and AI technology, your project aligns perfectly with my skillset. Moreover, with a focus on scalable and maintainable architectures, I will ensure that your V1 foundation is impeccable. I'll build a modular Python-based agent framework that consists of a main agent and at least one sub-agent capable of efficiently delegating structured tasks. Furthermore, my plan includes integrating LLM through an API for smooth functionality. Having built similar systems in the past, including AI agents and automation platforms with private LLM setups, I understand the intricacies of these projects. Some of my notable works include developing chatbots, leveraging LLM integrations, deploying models, and creating automation tools using services such as n8n and Zapier. With an estimated timeline of Phase 1 in mind, I propose a fixed fee for the project implementation despite its hourly nature. Your timeline is my priority as I am ready to move quickly once we align on the scope. My aim throughout will be to ensure a smooth knowledge transfer so you can operate and extend the system independently after the project is completed. Let's talk in more detail to get started right away!

@amadorjay

Hello, I will set up a secure, private AI-powered automation system on your Ubuntu VPS. This includes a modular Python-based agent framework with LLM integration. I have extensive experience building similar systems. I created a Python automation platform that integrated with GPT-3 and another that managed multiple AI agents effectively. **My approach will include:** - Secure Linux server setup with Docker. - Development of a main agent to handle structured tasks. - API integration for LLM (e.g., Claude). - Lightweight web interface for submitting natural language instructions. - Structured JSON output for easy data handling. - Comprehensive logging and scheduled task capabilities. **Questions for clarity:** - What specific LLM API do you prefer, and are there any constraints? - How many sub-agents do you anticipate needing in Phase 1? - What is your preferred format for documentation and knowledge transfer? I’m ready to start immediately. I’ll provide a high-level architecture, an estimated timeline, and a fixed price for Phase 1 upon agreement on scope. Let’s build a solid foundation together.

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@mahamnoornaz06

Hi there, with 5+ years in DevOps and AI automation systems, I can set up a secure Ubuntu 24.04 VPS foundation using Docker (no Kubernetes), implementing a modular Python agent framework with a master agent delegating structured JSON tasks to sub-agents, Claude API integration, logging, scheduled jobs, and a lightweight web UI. I prioritize clean architecture, container isolation, firewall hardening, and documented knowledge transfer so you can operate independently. Estimated 25–35 hours, 7–10 days, fixed price provided after scope confirmation. Should we use FastAPI for the interface? Do you prefer Redis or Postgres for task/state storage? If I am lucky enough to capture your attention, please don't hesitate to contact me. We can engage in a 10-15 minute discussion via messages to delve deeper into the project requirements and cover all aspects.

@nathanc97

Your requirements are clear: a secure, modular AI agent foundation without overengineering. High-level architecture: • Hardened Ubuntu + UFW + Fail2ban • Docker + Docker Compose (no Kubernetes) • Python (FastAPI) backend • Agent core layer (task parser + delegator) • Sub-agent module interface (plug-in style) • LLM client wrapper (Claude API abstraction layer) • Structured JSON schema validator • Lightweight web UI (React or HTMX) • Redis for lightweight task queue & scheduling • Centralized logging + rotating logs Estimated hours: 25–35 Fixed price: $900 Timeline: 7–10 days Deliverables include full documentation, architecture diagram, and live walkthrough training so you can confidently extend it yourself. Experience includes building containerized AI automation systems, private LLM wrappers, and modular task delegation frameworks with secure VPS deployments.

@bushrar60

As a highly skilled and experienced freelancer, I have the technical acumen and knowledge of automation frameworks to set up a secure, private AI server on your VPS. With a comprehensive understanding of DevOps and Docker, I can create a clean, scalable system that aligns perfectly with your project requirements. My expertise in Python-based backend development is exactly what you need for building your main and sub-agents. Importantly, I have worked on multiple projects integrating LLM APIs similar to what you're requesting. So, I would easily facilitate the incorporation of features like Claude into your system through seamless API interactions. Given my proficiency in documenting projects efficiently, I will make sure you receive clear instructions and knowledge transfer during our training sessions. Collaborating with me guarantees strict adherence to your constraints including: - No automatic live pushing of changes to production - No browser automation - Clean, modular & maintainable architecture - A solid V1 foundation that will serve as an excellent operational base for any future development.

@jeremiasg17

Hello, I can design and deliver a clean V1 AI automation foundation on your VPS with a strong focus on security, modularity, and long term maintainability. High level architecture Ubuntu hardened with UFW, SSH key only access, fail2ban, Docker rootless setup. Docker compose stack with: reverse proxy, FastAPI backend, worker service, Redis for task queue, and optional Postgres or SQLite. Core Python agent framework built around a planner executor pattern. Main Orchestrator Agent that converts natural language into structured task plans. Registered sub agents with strict Pydantic schemas and enforced JSON outputs. LLM abstraction layer supporting Claude via API with retry logic and validation. Lightweight web interface for submitting instructions and reviewing structured results. Centralized logging, run history, and basic scheduled jobs using APScheduler. Clear documentation and guided knowledge transfer session. I can begin immediately and deliver a clean, production ready V1 foundation you fully control and understand.

@AngelusCross

I've read your requirements and I have direct experience with setting up a secured Linux server with Docker and AI API integrations. My background largely is from working in hyper-scale AI data centers, network operations centers, and a personal home lab focused on AI-enabled services. I will deliver a production-ready system without unnecessary complexity, along with full documentation, a basic set of automation scripts, and a handover session so you can manage and extend it easily. My rate for this project would be $15 per hour or a maximum of $200 if additional work is needed beyond phase 1. What I propose for the scalable private AI agent server: - Deployment: Docker Compose for clean, reproducible service management - Web UI: OpenWebUI - self-hosted, Docker-native polished interface with built-in user management, model switching, and native RAG vector database for knowledge base and logging - Agent Automation: Python pipelines + MCP servers for modular tasks, potential for low-code N8N (Docker) deployment for agentic task automation - LLM Integration: API-based (Anthropic, OpenAI, or preferred AI provider) to keep costs low, with a clear path to open-source self-hosted models (Ollama, LMStudio, Vllm, etc) for full data security if needed - Security: Hardened Linux server setup with network isolation, secrets management, access controls, server and Docker management web interfaces

@saic1

Hi, I have solid hands-on experience in DevOps, Docker-based infrastructure, secure Linux server setup, and Python backend development, along with integrating LLM APIs like Claude and OpenAI. I can design and implement a clean, modular, production-ready V1 foundation on your Ubuntu 24.04 VPS — without Kubernetes and without unnecessary complexity. I fully understand that this is a setup + knowledge transfer project, not long-term management. My focus will be on clarity, security, modularity, and making you fully capable of operating and extending the system independently.

@ivaylo8

Dear Client, I am excited to help you set up your private AI-powered automation system on your Ubuntu VPS. With my experience in DevOps and automation, I can ensure a solid foundation that meets your specifications. For Phase 1, I propose a structured architecture including secure server setup with Docker, a Python-based agent framework, and integration with an LLM API such as Claude. The main agent will skillfully delegate tasks to sub-agents while a lightweight web interface will allow you to input natural language instructions. Documentation will guide you through the system for your independent management. I anticipate about 40 hours for Phase 1 to cover all setup, training, and documentation thoroughly. I propose a fixed price of $150 for this work, aiming to streamline the implementation and knowledge transfer process. I have successfully built similar systems, ensuring clean and maintainable architectures without Kubernetes or live auto-deploys. Let's solidify your V1 foundation! Looking forward to your response, Ivaylo

@ravikumarsingh1

I can design and deploy a secure, private AI automation foundation on your Ubuntu VPS with a clean, modular architecture and full knowledge transfer. Proposed Architecture: • Hardened Ubuntu + Docker Compose stack • Python FastAPI backend (agent controller) • Main agent + delegated sub-agent via task router • LLM integration (Claude/OpenRouter API) • Lightweight web UI for natural-language tasks • Structured JSON output layer • Redis/file-based queue + logging • Scheduled jobs via cron worker container Experience: Built self-hosted AI agents, n8n/LLM automation systems, and private Docker-based AI backends with secure VPS deployments and training handoffs. Phase 1 Estimate: • 18–24 hours total • Fixed price: $10 • Timeline: 4–5 days Includes documentation, walkthrough session, and operational training so you can extend the system independently.

@Hemantt841

With over 15 years of experience in software development and digital solutions, I have successfully delivered 200+ projects for startups, mid-size companies, and enterprise-level clients across various industries. My expertise in DevOps, Docker, and Python-based backends uniquely positions me to tackle the challenges of your project effectively. For your AI-powered automation system, I will focus on creating a secure and scalable architecture that aligns with your Phase 1 goals. The setup will include a modular Python-based agent framework, where the main agent delegates tasks to sub-agents, ensuring clean and maintainable code. Integrating a lightweight web interface for natural language instructions will enhance usability, while structured JSON outputs and logging capabilities will keep everything organized and efficient. Documentation will be a key