Senior AI & DevOps Engineer – Air-Gapped Enterprise AI Architecture (Fixed Fee + Future Equity Potential)

@InterconnectBPO

The goal is to create a secure, air-gapped Enterprise Analytics & AI System that meets your stringent requirements. Our team specializes in building robust data ingestion pipelines and deploying local AI models in fully offline environments, ensuring high security and performance. For your project, we will utilize a vector database like Qdrant to efficiently parse and index corporate documents. The deployment of local LLMs via Docker will guarantee functionality without internet access, while our expertise with React will deliver an engaging dashboard for real-time insights. Our experience with Open-Source LLMs, Docker, and modern web technologies makes us a perfect fit for this project. Also, communication, quality, and on-time delivery are priorities. If you'd like, I can also share similar work we've completed and discuss the best approach for your project. Regards, JP

@sunainawbst

✅DevOps| Lovable AI Expert | AI Development | Game Development ✅ Hi, Thank you for considering this opportunity! I bring extensive experience in implementing custom solutions powered by LLMs, conversational AI, and intelligent automation. Recently I have been working on Lovable AI for developing a gaming platform using it, complete with chat-based agent logic, expressive front-ends, and backend integrations. See here : In other project, implemented a fully automated AI agent system for intelligent meeting creation using ElevenLabs Conversational AI and Gemini (via a custom agent brain). The flow integrates voice interaction, natural language processing, location precision, and frontend. Whether you're building an internal assistant, a public-facing voice agent, or an integrated AI productivity tool, I can help bring your vision to life with robust, scalable architecture and a human-like user experience. I am an Expert in following: Docker, Jenkins and Kubernetes Chef Aws (EC2, S3, Route53, VPC, IAM, RDS, Cloud Formation, CloudFront, AWS Autoscaling, Aws Codedeploy, Aws Codepipeline, Powershell etc.), Microsoft Azure, Google Cloud Platform Amazon Linux, Ubuntu, Centos Terraform development CI/CD pipelines I would love to connect and explore how we can contribute to your AI initiative. Regards Ranjana

@MashoodurRehman1

I am a seasoned Senior AI & DevOps Engineer with extensive experience in designing secure, air-gapped systems. My expertise aligns perfectly with your project's requirements, particularly in using Open-Source LLMs, Local RAG frameworks, and Vector Databases. I have a proven track record in leveraging Docker and Docker Compose for deploying containerized applications, ensuring offline operational functionality. In previous projects, I have successfully implemented data ingestion pipelines and developed user-friendly dashboards using React and Next.js. My familiarity with Open-Source Vector DBs such as Qdrant and Chroma equips me to handle the data parsing and indexing needs effectively. Additionally, my adherence to strict security protocols and experience with NDAs assure the safety and confidentiality of your enterprise data. I am interested in the potential for a long-term partnership and would appreciate the opportunity to discuss the strategic equity offer. Could we schedule a time to discuss the milestones and specific project deliverables? Thank you for your consideration.

@Mahihassi

As an experienced AI and DevOps engineer, I have worked extensively on projects akin to yours. My proficiency in building scalable backend systems, AI-powered platforms, and modern web dashboards is what sets me apart. My work has often revolved around Python, FastAPI, Node.js, React, cloud platforms, and database systems - the exact skills you require for this project. To delve into my background further, I have an advanced mastery of Docker and Docker Compose along with considerable experience in offline deployments. This will prove invaluable in the air-gapped nature of your Enterprise Analytics & AI System. Additionally, my understanding of building modern web dashboards using React will facilitate the development of a sleek executive front-end dashboard that you require for your project. Moreover, I fully comprehend the urgency of maintaining tight security measures and therefore hold great respect for NDA and IP Assignment Agreements. Being a startup enthusiast myself, I understand the significance of initial stages like MVP and am eager to nurture long-term relationships with potential partners. By selecting me for this project not only will we deliver a fully functional local environment (MVP Stage), but also open avenues for future collaboration as technical co-founder or through equity partnerships.

@AndrewJSMAN

I’ve architected and deployed multiple air-gapped RAG systems using Ollama, Qdrant, and Docker Compose for offline LLMOps pipelines—handling enterprise PDF/Excel ingestion, vector indexing, and LLM serving with zero external dependencies. I’ll spin up a modular ingestion pipeline (PyMuPDF for parsing, Qdrant for vector store) behind locked Docker Compose, deploy Ollama-based LLMs with pre-loaded models in isolated containers, then expose REST endpoints for the React dashboard. Authentication and NDA-compliant asset handling are built into the stack from day one. Milestones align with your phased delivery; I can start immediately. Thanks, Andrii.

@ozbas

As a Full-Stack software engineer with over a decade of experience, I am equipped with the extensive skills required to tackle your unique project. Particularly, my expertise in developing Hybrid apps using Flutter and React Native can be employed to construct your necessary local LLMs. I have a deep understanding of Dockerization and offline deployment setups—a testament of my mastery of Docker and Docker Compose frameworks. I have also been successful in building modern Web Dashboards like you require using popular technologies such as React and Redux. Moreover, throughout my career I have practiced a transparent and consistent line of communication; always keeping my clients informed about project progress. This would translate into your experience with me - you’ll be updated daily on the status of the project as it progresses. I also provide free technical support once the app is successfully completed in addition to providing well-commented source code for future maintenance. In summary, my expansive skill set combined with my years of experience uniquely prepare me to not only complete this valuable MVP stage but also assume a larger role as your Technical Co-Founder should we align on future expansion. Let's take the first step towards transforming your air-gapped enterprise architecture solution into reality by discussing further details about your project. Thank you in advance for considering me for this significant undertaking! Godspeed.

@rbtech1984

Hello!! Got the requirements, a secure air gapped Enterprise AI platform with offline RAG, local language models, Docker deployment, and a modern dashboard will be delivered within your milestone timeline. • Which document types will be used most often? • Do you already have the server environment prepared? • Which local language model do you prefer for the first release? A reliable and scalable solution will be built with secure data ingestion, vector database integration, offline AI deployment, and a clean dashboard designed for smooth enterprise use. Relevant AI, DevOps, Docker, and modern web application projects have been completed successfully, and building secure systems that perform with confidence is always the priority. Looking forward to discussing your project and getting started. Best regards Farhin B

@mughiraaa

Hello, I have already completed similar AI, web, and enterprise software projects involving local AI deployment, document processing, APIs, and scalable software solutions. I have strong experience in web development, APIs, Docker, local AI environments, and scalable software solutions. I can quickly understand your requirements and deliver a secure offline system with document ingestion, vector search, local LLM integration, and a clean dashboard for analytics and AI interaction. Will the system need to support multiple local AI models from the beginning, or will the MVP focus on a single model with future expansion? I would be happy to sign your NDA, discuss the project in detail, and help turn your MVP into a solid foundation for future growth. Let's schedule a quick meeting. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@albertc1996

Hey this project lines up closely with work I've already been doing so happy to dive in I've spent time building in this exact space so offline first AI system design local RAG pipelines and containerized deployment isn't new territory for me it's a problem space I already work in regularly I've also built low level systems software focused on reliability and being fully self contained without depending on external infrastructure which is really the core challenge of an air gapped system like this one For the ingestion and RAG side I'd set up a pipeline handling PDFs Excels and Docs indexed into an open source vector DB like Qdrant or Chroma depending on which fits your query patterns and scale better then wire that into a locally hosted LLM through Ollama or vLLM packaged entirely in Docker and Docker Compose so the whole stack comes up cleanly with zero external calls at any point that's the part I'd stress test the hardest since 100 percent offline functionality is easy to claim and much harder to actually guarantee under real conditions For the front end I'd build a clean React or Next dashboard that surfaces status metrics data insights and the local AI assistant in a way that feels fast and native rather than bolted on talking to the backend through a proper REST layer I'm fully comfortable signing the NDA and IP assignment before starting given the nature of this build that's expected on my end Happy to walk through relevant past work in more detail on a call and talk through milestone structure for the 4 to 6 week timeline including how I'd approach the long term co founder conversation once the MVP stage proves out

@ImranAli01

Hello, I got that you are looking for a secure, fully offline Enterprise AI platform with a local RAG pipeline, containerized LLM deployment, and an executive dashboard that runs entirely in an air-gapped environment. This is what I can help you with, let's chat. My approach is to build the solution using Docker Compose, Ollama or vLLM, Qdrant, FastAPI, PostgreSQL, and React or Next.js. The key challenge is ensuring true offline operation, so I will create a self-contained architecture where document ingestion, embeddings, vector search, and LLM inference all run locally with zero external dependencies. The system will support PDFs, Excel, and Word documents, expose secure REST APIs, and provide a responsive AI assistant and executive dashboard. The architecture will be modular, making future enhancements simple without major redesign. As final deliverables you will receive a fully containerized offline AI environment, local RAG pipeline, vector database, executive dashboard, source code, Docker Compose files, deployment documentation, and setup instructions. I am happy to work under an NDA and IP Assignment Agreement. One thing I'd like to confirm before we start: which local LLM do you plan to use, such as Llama, Qwen, or Mistral? Let's schedule a quick chat to discuss your infrastructure and milestones. Best Regards, Imran

@amadorjay

Hi there, The hard part is not just building the offline AI system; it’s ensuring robust data ingestion and local deployment without external access. My approach would prioritize the pipeline architecture for ingesting and indexing corporate documents, ensuring it integrates well with the chosen open-source vector database. Following that, I’d focus on containerizing the local LLMs to guarantee full functionality in an air-gapped environment. For the executive dashboard, I can create a responsive interface using React that effectively visualizes project metrics and insights while maintaining a clean aesthetic. What specific corporate documents will be prioritized for ingestion? Thank you.

@lazarr4

Hello, Building a fully air-gapped AI system means handling sensitive corporate data without any external dependencies, which requires careful containerization and local-first architecture. I’ve deployed similar offline RAG pipelines for defense contractors, using Qdrant as the vector store and Ollama for LLM inference with Docker Compose—only minor adjustments were needed for their air-gapped Kubernetes cluster. I’d approach this with a layered Docker setup where the vector DB, LLM server, and React dashboard run in separate containers, communicating via a local REST API. The biggest improvement will come from pre-configuring all dependencies in a single offline Docker image to eliminate runtime surprises. Your MVP will deliver a self-contained analytics dashboard that executives can use without internet access, ensuring data stays secure while maintaining full AI functionality. Happy to discuss the NDA and technical details whenever it’s convenient for you. Thanks, Lazar.

@longngoc2307

Hello. "GOHIGHLEVEL FUNNEL BUILD" - you need accurate CRM workflows from your detailed specs. I'll build native GHL funnels, forms, and workflows with strict field mapping because duplicate fields and wrong triggers can break lead routing. I'll focus on custom fields, workflow branches, pipelines, and mobile testing. I will verify form submissions, trigger visibility, snippets, and page behavior before launch. Can we review the milestone one requirements first? Thank you.

@Qloron0804

Hi, I went through your requirements, and we already have a Senior DevOps Engineer available who closely matches the skills you are looking for. Our engineer has hands on experience with Docker, Kubernetes, Terraform, Ansible, Linux, CI/CD, Git, Python, Bash, Infrastructure as Code, and secure enterprise deployments. They have worked on production environments, automated deployment pipelines, and complex infrastructure projects where stability and security were critical. While we are new on Freelancer.com, we are an established IT company with a strong technical team and have successfully delivered similar enterprise projects for our clients. Our approach is simple. We will first review your existing environment, understand the current setup and challenges, and then prepare a clear execution plan before making any changes. This helps reduce risk and ensures a smooth implementation. Our engineer can work during your business hours, collaborate directly with your team, communicate clearly in English, and start immediately. If you would like, we can share the developer profile and arrange a technical interview right away. Looking forward to discussing your project. Best Regards, Nikhilesh Team Qloron

@naminghn

Hi there, I understand you're looking for a secure, fully offline/air-gapped Enterprise AI system. I can build the data ingestion pipeline utilizing Open-Source Vector databases like Qdrant, and I have strong expertise with Docker and Local LLM deployments. My experience aligns well with creating responsive dashboards in React, ensuring a seamless user interface for your project metrics and AI assistants. Your satisfaction is my priority and I guarantee that I will deliver you a high-quality result. Regards, Ali

@ahmaddar2970

Eager to leverage my expertise in AI and DevOps to craft a secure, air-gapped enterprise AI system tailored to your needs. With extensive experience in deploying open-source LLMs, containerization, and designing sophisticated dashboards, I am confident in building an efficient MVP for your innovative project. I understand the importance of data security and system integrity in your environment and will ensure a streamlined, milestone-based delivery within 6 weeks. Looking forward to collaborating on this transformative journey toward strategic growth and long-term partnership. What specific security requirements or compliance standards should be prioritized in your air-gapped environment?

@lakshithap1

Leveraging on my diverse technical background and resilient problem-solving skills, I believe I am the ideal candidate for your Senior AI & DevOps Engineer role. My extensive hands-on experience in web and mobile app development, AI agent development, and desktop application, using technologies such as React.js, Python (for data extraction and mining), Docker, and more aligns perfectly with your project requirements and will ensure a smooth execution of the tasks. Having worked on both frontend and backend projects, including complex user management systems, admin dashboards, and travel management systems using Java (AWT/Swing), I am well-versed in creating dynamic interfaces that enhance user experiences- an aspect critical for your project's frontend dashboard. My dedication to continuous improvement, compliance with Non-Disclosure Agreements, together with my consistent delivery of high-quality work assures you an incredible partnership if selected. Let's revolutionize enterprise analytics together!

@shubhi8982

As a seasoned Full-Stack Developer with a deep understanding of the entire development process, I am uniquely qualified to take on the challenge of building your secure, air-gapped Enterprise AI architecture. I understand that every element of the system, from data ingestion to AI deployment and front-end display, needs to be meticulously executed to ensure 100% functionality without external internet access - something I have considerable experience with. My skills in Docker and Docker Compose, honed through numerous offline deployment setups, align seamlessly with your project's requirements. In addition to my technical proficiency, I bring a strong sense of commitment and ownership to every task I undertake. I understand the sensitive nature of your project and hence can comfortably sign the NDA and IP Assignment Agreement prior to commencement. Moreover, my experience in Python and local LLMs will be valuable in delivering an efficient pipeline for ingesting, parsing, and indexing corporate documents.

@roell3

Hi, I build production ML systems and Python backends, including local LLM stacks, RAG pipelines, and containerized deployments in restricted environments. The hard parts here are ensuring deterministic behavior in an air‑gapped setup, especially around model packaging and dependency isolation. Document ingestion quality and consistent indexing across varied file types will affect retrieval reliability. There’s also the challenge of maintaining observability and reproducibility without external services. How are you handling updates to models and datasets once deployed offline? What are the expected document volumes and refresh frequency? Do you already have constraints on hardware or runtime SLAs for inference and indexing? Happy to discuss next steps.