Kubernetes AI CICD Demo Application

@zeeshanmcp12

I aim to develop a Kubernetes AI CICD Demo Application that showcases Nvidia AI Enterprise, LLM functionality, and Retrieval-Augmented Generation on Kubernetes, with a focus on CI/CD automation, auto-scaling, and FinOps integration. My approach includes setting up a clean repo, implementing CI/CD pipelines with GitHub Actions, managing drift with Helm/ArgoCD, and enabling auto-scaling for CPU and GPU requests. With experience in deploying Nvidia AI Enterprise stack and integrating FinOps dashboards for live cost tracking, I am well-equipped to deliver a functional proof-of-concept that meets your specified requirements. Thank you.

@jagratip3

Hi, I have 10+ years of experience in cloud, Kubernetes, DevOps, and AI/ML platform development. I can build a complete MLOps proof-of-concept that includes Terraform-based infrastructure (GKE/EKS/AKS), GitHub Actions CI/CD, ArgoCD/Helm GitOps deployment, Nvidia AI Enterprise integration, LangChain-powered agents, RAG with an open-source vector database (Milvus/FAISS), and FinOps visibility using Prometheus and OpenCost. The solution will demonstrate automated deployment from a clean repository, CPU/GPU autoscaling, RAG-powered question answering with citations, tool-calling agents, token/cost tracking, and reproducible infrastructure as code. I can share similar Kubernetes and AI platform work and am ready to start immediately. Best regards

@laibatahir396

Hi there, As a DevOps/MLOps engineer with hands-on experience in Kubernetes, Terraform, GitHub Actions, ArgoCD, GPU-enabled workloads, and AI platform deployments, I can build a complete proof-of-concept MLOps environment that demonstrates the full lifecycle from code commit to production deployment. I have experience deploying cloud-native AI solutions using Kubernetes, GitOps, Infrastructure as Code, and observability stacks, and can integrate NVIDIA GPU operators, Triton Inference Server, LangChain-based agents, vector databases such as Milvus or FAISS, and RAG workflows into a unified platform. The solution will include Terraform-based cluster provisioning (EKS/GKE/AKS), CI/CD pipelines, Helm charts, ArgoCD configurations, autoscaling for CPU and GPU workloads, Prometheus/OpenCost-based FinOps metrics, and demonstration notebooks or API scripts showcasing inference, RAG enrichment, citation generation, token consumption, and cost tracking. My focus is on delivering a reproducible, open-source-first architecture that can be deployed on a clean environment and serve as a foundation for future enterprise-grade AI and MLOps initiatives. Best Regards Laiba

@Deepalitech91

Hello, I can build a complete MLOps proof-of-concept that demonstrates modern AI infrastructure on Kubernetes, combining CI/CD, GitOps, Nvidia acceleration, LangChain agents, RAG, autoscaling, and FinOps observability. ✔ Terraform-based GKE/EKS/AKS provisioning ✔ GitHub Actions CI/CD pipeline with automated testing and deployment ✔ ArgoCD/Helm GitOps workflow and drift management ✔ Nvidia GPU Operator, Triton Inference Server, TensorRT, and CUDA integration ✔ LangChain-powered agents with external tool calling ✔ RAG implementation using FAISS or Milvus ✔ GPU/CPU autoscaling with HPA/KEDA ✔ Prometheus, Grafana, and OpenCost for token/cost visibility ✔ Demo notebooks and API scripts showcasing inference, RAG citations, token usage, and cost metrics The solution will be modular, documented, reproducible from a clean repository, and designed for future production hardening. I can start immediately and deliver incremental milestones for rapid feedback and iteration. Best Regards

@ViktorTrefilov01

Hi, Do you already have a preferred cloud provider for the first demo, GKE, EKS, or AKS? I understand you need a working Kubernetes MLOps proof of concept with CI/CD, Terraform, Helm or ArgoCD, Nvidia AI Enterprise components, LangChain agents, RAG, autoscaling, and FinOps-style cost metrics. I have strong experience building full-stack AI systems with practical backend logic, API workflows, AI integration, automation, and production-focused architecture, so I can help turn this into a clean demo repo that is easy to run, review, and extend. You should select me because I focus on building usable proof-of-concepts, not just theoretical infrastructure, with clear documentation, reproducible setup, and clean separation between app, pipeline, cluster, and AI services. I can start with Terraform for the cluster, then add GitHub Actions, Docker build, Helm charts, ArgoCD sync, LangChain APIs, vector search, Prometheus/OpenCost metrics, and demo curl scripts or notebooks to prove the full workflow. Please address this in the next project so we can continue smoothly. Best regards

@sgwtechnology

Hello, This project aligns closely with my experience in MLOps, Kubernetes, LLMOps, RAG systems, and cloud infrastructure. I can deliver a complete proof-of-concept featuring Terraform-based infrastructure (GKE/EKS/AKS), GitHub Actions CI/CD, ArgoCD GitOps, NVIDIA AI Enterprise components (Triton, TensorRT, CUDA), LangChain/LangGraph agents, and an OSS vector database such as Milvus or FAISS. The solution will demonstrate end-to-end deployment, GPU/CPU autoscaling, RAG-powered question answering with citations, multi-tool agent workflows, and FinOps visibility using Prometheus/OpenCost. All components will be infrastructure-as-code, reproducible from a clean repository, and fully documented with demo notebooks, API examples, and deployment guides. I focus on production-grade architecture, observability, scalability, and maintainability, and I can deliver incremental milestones for rapid validation and hardening.

@mobasherreza10

As an AI and ML enthusiast with a strong background in DevOps, Docker, Kubernetes, and LangChain, I am ideally suited to deliver your ambitious proof-of-concept application. My hands-on experience with the various tools and technologies you've outlined matches this project's every requirement. Having worked intensively with Helm, I am adept at ensuring the desired state remains in sync, making sure every piece in the complexity of MLOps stack works together elegantly. Furthermore, my proficiency in Terraform and similar Infrastructure-as-Code scripts will facilitate the creation of a stable base cluster for GKE/EKS/AKS. I appreciate the essence of keeping everything as open-source as possible as it promotes flexible iterations, thereby maximizing project value. This aligned strictly with my approach to project delivery; pushing early versions to gather feedback and hardening them together.

@hakan1022

Hello, I think your project is a complex but exciting challenge that I have successfully tackled in a previous project, where I built a similar infrastructure for a machine learning application. In that project, I designed an end-to-end CI/CD pipeline using GitHub Actions, deploying a containerized application on a Kubernetes cluster with Terraform for infrastructure as code. One significant challenge was ensuring that the application could auto-scale based on workload, which I achieved by implementing Horizontal Pod Autoscaling and configuring resource requests for both CPU and GPU. To address your requirements, I propose a system where I will set up a Kubernetes cluster using Terraform, configure CI/CD pipelines with GitHub Actions, and manage deployments with Helm or ArgoCD. The architecture will include Nvidia AI Enterprise components, LangChain for LLM endpoints, and a vector database for RAG. I will also implement monitoring for token usage and cost metrics, integrating Prometheus or OpenCost for FinOps tracking. If I use my previous experience, your project will likely be completed successfully. Hope to discuss this in detail. Through detailed discussion, I think I can find the better solution to finish your project successfully. Thank you

@oleksandrn28

Hi there, I am excited about the opportunity to work on your project and develop a cutting-edge proof-of-concept application that aligns with your vision for a modern MLOps stack on Kubernetes. I have extensive experience in building CI/CD pipelines, deploying on Kubernetes clusters, and integrating AI technologies into scalable solutions. To meet your requirements, I will leverage my expertise in Terraform to create Infrastructure as Code scripts for setting up the base GKE/EKS/AKS cluster. I will design robust CI/CD definitions, Kubernetes manifests/Helm charts, and configure ArgoCD for drift management. Additionally, I will develop demo notebooks and scripts to showcase the AI pillars you are interested in, along with implementing autoscaling capabilities and tracking cost metrics for FinOps analysis. My approach will focus on open-source tools and technologies wherever possible, ensuring transparency and flexibility. I will closely collaborate with you to iterate quickly, delivering early versions for feedback and refinement to meet the acceptance criteria. I am confident that my skills and experience make me the ideal candidate to bring your project to life. Let's work together to create a successful proof-of-concept application that exceeds your expectations. Looking forward to collaborating with you, Oleksandr

@Sanacoder26

I specialize in web development with strong experience in HTML, CSS, JavaScript, PHP, and backend integrations. I focus on clean code, security, and maintaining compatibility with existing systems. For this project, I can deliver a complete reCAPTCHA integration, proper server-side validation, and clear documentation while ensuring that the current login workflow remains stable and user-friendly.

@sanddep5

Hi, This is exactly the kind of infrastructure-heavy build I enjoy working on. You’re not asking for a simple demo — you need a proper working MLOps pipeline that proves deployment, scaling, RAG, agent workflows, and cost visibility all in one place. That’s the right way to validate a stack before production. My approach would be to build this in layers: • Provision the cluster with Terraform (GKE/EKS/AKS based on your preference) • Set up GitHub Actions for testing, image builds, and deployment • Use Helm and ArgoCD so the cluster stays aligned with Git and drift is managed properly • Deploy GPU workloads using Nvidia operator and configure Triton/TensorRT where needed • Build the LangChain service with two working agent flows: – RAG-based Q&A over a vector store – Multi-tool planning/action workflow • Add Milvus or FAISS for embeddings and retrieval • Wire Prometheus + OpenCost so you can actually see usage and cost per request • Configure autoscaling and load testing to validate CPU/GPU elasticity What I like about this setup is that it stays practical — everything is modular, reproducible, and easy to extend later. A few things I’d want to confirm first: Which cloud are you leaning toward — GKE, EKS, or AKS? Do you already have Nvidia AI Enterprise licensing? Should the demo LLM run open-source (Llama/Mistral) or connect to OpenAI/Anthropic? Best regards, Sandeep

@juan99lizano

You need a working MLOps proof-of-concept on Kubernetes — Nvidia AI Enterprise (Triton/TensorRT), LangChain agents, RAG with a vector store, GitOps CI/CD, autoscaling, and FinOps metrics — all spinning up from a clean repo. I've built similar stacks: GKE cluster via Terraform, ArgoCD managing Helm releases, GPU node pools with the Nvidia operator, and Prometheus/OpenCost exporting per-request cost data. Reduced infra drift incidents to zero on one client's ML platform by enforcing GitOps strictly. I can deliver: Terraform (GKE/EKS), GitHub Actions pipeline, Helm charts for Triton + LangChain service + vector DB (Milvus), two demo agents (RAG Q&A + multi-tool loop), HPA/VPA configs, and OpenCost integration — with curl/notebook demos hitting all endpoints. Timeline: 10–14 days with early pushes for iteration. Are you targeting a specific cloud provider (GKE/EKS/AKS), or is that still open?

@bondoc0526

༺❖༻ Dear Client ༺❖༻ Thanks for posting about my specialist job area. Your requirements perfectly match my experience in Kubernetes, MLOps, and AI infrastructure. I’ve built cloud native ML systems using Docker, Kubernetes, and CI/CD pipelines where models were deployed through automated workflows, scaled dynamically, and exposed as APIs. I also implemented RAG based systems using vector databases for retrieval augmented LLM responses. I can build your proof of concept using Terraform for cluster setup on GKE, EKS, or AKS, plus GitHub Actions for CI/CD and Helm or ArgoCD for deployment management. Inside Kubernetes I will deploy a LangChain powered LLM service connected to FAISS or Milvus for RAG workflows. I will also configure autoscaling for CPU and GPU workloads and add basic FinOps tracking using Prometheus or OpenCost style metrics. I can start immediately and deliver a working end to end demo. Best regards Glenn Bondoc

@nataliyabest1

Hello, I read your brief closely. You want a working proof of concept that brings a full MLOps stack onto Kubernetes, from a clean repo through CI/CD to a live demo covering three pillars: Nvidia AI Enterprise for accelerated inference, LLM functionality with LangChain, and RAG backed by a vector store. Here is my approach. I would write IaC to stand up a managed cluster with a GPU node pool. CI/CD runs tests, builds the image, and promotes it, with ArgoCD keeping desired state in sync. Horizontal pod autoscaling on CPU and GPU signals shows the cluster resize under load. Inside the cluster I would install the Nvidia operator for Triton and CUDA, wrap LLM endpoints with LangChain so agents can call tools, and use an open source vector database for embeddings. One agent answers questions over the store with citations, another runs a plan and act loop. Token usage is tracked per request and exported through Prometheus and OpenCost for FinOps. Delivery includes the IaC, CI/CD, Helm charts, ArgoCD config, and demo scripts showing output, RAG enrichment, token usage, and cost. One honest note. The scope is large for the budget, so I suggest a first milestone that gets the pipeline green and one agent working, then we harden the rest together as you mentioned. I keep everything open source where possible. Tell me your cloud preference and we can start. Best regards, Nataliya Huley

@sniegul

I'm excited to offer you a proof-of-concept application that integrates a modern MLOps stack on Kubernetes. By leveraging state-of-the-art tools like TensorFlow and PyTorch, along with robust CI/CD pipelines, we can demonstrate how to efficiently train, deploy, and monitor machine learning models in a scalable and automated manner. This demo will include everything you need: from setting up the Kubernetes cluster to deploying your AI models using Jenkins for continuous integration and GitHub Actions for continuous delivery. The result will be a seamless workflow that ensures your models are always up-to-date and ready to serve predictions at scale. To get started, let's schedule a call to discuss your specific requirements and timeline. I'm looking forward to bringing this exciting project to life together! Jakub

@iterativeTech

As a seasoned AI and Cloud Data Engineering Specialist with a decade-long track record of deploying cutting-edge solutions, I believe I am the perfect fit for your Kubernetes AI CICD Demo Application project. My expertise spans both AI/ML Development and Cloud Data Engineering - matching up nicely with the two core aspects of your project. I'm well-versed in using technologies such as Docker, Kubernetes architecture, and Terraform to build scalable, performant systems that align with business objectives - skills you'll find invaluable in executing the base GKE/EKS/AKS cluster setup and manifest definitions this project requires. With a deep understanding of the entire CI/CD process, including building & testing containers and Helm charts' configuration, I'm well-equipped to ensure smooth drift management as desired by you through Helm or ArgoCD. Moreover, my knowledge extends beyond just building pipelines; I've ventured into intelligently automating key facets of businesses using AI agents, LLMs, and API-driven systems which can be put to good use in this project.w

@raj00565

With strong expertise in Kubernetes, CI/CD, Terraform, Helm, ArgoCD, and cloud platforms (AWS, GCP, Azure), I can help build and automate your AI demo application from infrastructure provisioning to deployment. My experience includes Kubernetes scaling, GitOps workflows, Infrastructure as Code, GPU/CPU optimization, and cloud cost management. I focus on delivering scalable, maintainable, and production-ready solutions using both open-source and enterprise-grade technologies.

@NEHABHAT92

As an AI enthusiast with a decade of experience, I not only share your passion for developing modern MLOps stacks but have the skills and capabilities to pull off this project with perfection. My team and I have a deep understanding of Kubernetes and CI/CD pipelines using tools like GitHub Actions and ArgoCD. We are also proficient in creating Kubernetes manifests and Helm charts that align perfectly with your workflow requirements. What sets us apart is our extensive knowledge and hands-on experience with Nvidia AI Enterprise stack; from Triton and TensorRT to CUDA, we can admirably install and work with these components via the operator. Adding to this, we have expertise in vector database such as FAISS or Milvus which serves your crucial requirement of storing embeddings for Retrieval-Augmented Generation (RAG). Besides these, my team is skilled in Terraform-based Infrastructure-as-Code deployments for GKE/EKS/AKS, which means you are guaranteed accurate, easily accessible scripts for deploying the base cluster. We also understand the value of open-source solutions for you and will diligently work towards keeping your project as open sourced as possible while ensuring its efficiency. Trust us to deliver a high-quality, well-documented solution promptly and please note our budget-friendliness too.

@crystalhitesh

Hi, With 10+ years of experience in DevOps, Kubernetes, AI infrastructure, and scalable backend architectures, I can build this end-to-end MLOps proof of concept using open-source technologies. I have experience with Terraform, Kubernetes, CI/CD pipelines, LangChain, RAG architectures, GPU workloads, and observability stacks. I will deliver a reproducible environment with autoscaling, Nvidia AI integration, FinOps metrics, and production-style deployment workflows that can later evolve into a full enterprise-grade AI platform. Best regards, Hitesh Goyal