Expert Cloud Infrastructure & Deployment Engineer Needed

@gattes

Hi there, I have over 10 years work experience with LInux and cloud computing (over 10y with AWS) working as DevOps/SRE and I think I can definitely help you out. I will need quite a bunch of details before jumping into a project estimation/MVP. Ping me and lets discuss. Cheers!

@myGigs

Good afternoon! I'll design and deploy your Customer Auto-Triage System on AWS (or GCP if preferred), setting up Docker-based containerization, a CI/CD pipeline via GitHub Actions, NGINX reverse proxy, PostgreSQL deployment, SSL, secrets management, and logging with CloudWatch or equivalent. I recommend AWS for its mature autoscaling, managed services, and cost controls suited to an early-stage AI workload. An MVP deployment realistically takes 7 to 10 days. Any specific backend stack or existing partial services I should build around?

@sahilcomputers39

Hi, I’m a Cloud Infrastructure & Deployment Engineer with 16+ years of experience in AWS/DevOps and production deployments for SaaS and AI-driven platforms. I can help you take your Customer Auto-Triage System from MVP to a scalable production-ready cloud setup. How I can help: • Design secure cloud architecture (VPC, subnets, IAM, security groups) • Deploy backend APIs using Docker (ECS/EKS based on needs) • Configure DB (PostgreSQL/MongoDB), backups, and encryption • Set up CI/CD pipeline (GitHub Actions/GitLab CI/Jenkins) • Manage secrets using AWS Secrets Manager/SSM • Configure SSL + domain + reverse proxy (NGINX if needed) • Implement monitoring/logging (CloudWatch alerts, dashboards) • Optimize performance, uptime, and cost Deliverables: • Fully deployed infrastructure • Working production deployment pipeline • Documentation Recommendation: AWS (best managed services + scalability + cost control). Budget can be finalized after discussing scope. Best regards, SaD

@nadeemkhan7

Hi, Customer Auto-Triage System needs production-grade cloud readiness while still in MVP mode. You require secure, scalable infrastructure and repeatable deployment—not just theory—to run AI workflows and route queries reliably with minimal ops overhead. I have deployed similar AI triage backends using AWS and GCP with Docker, RESTful APIs, PostgreSQL/MongoDB, CI/CD, NGINX, secrets management, and autoscaling. For this system, I recommend GCP for faster AI/ops tooling, lower egress for models, and managed services that keep MVP cost and complexity low while enabling clean scale-out. I will containerize services, wire CI/CD, enforce SSL and access control, and instrument logging and cost controls. I can deliver a fully deployed MVP with monitoring and documentation within 7–10 days. Knowledge transfer and runbooks are included so your team can iterate confidently while maintaining uptime and security. Lets connect in chat so that we discuss further. Regards, Mohd Nadeem Khan

@ravikumarsingh1

You’re at the exact stage where good infrastructure decisions will either scale your product—or slow you down later. I can help you set up a clean, production-ready foundation. 1. Recommended Cloud Platform I recommend AWS: Best ecosystem for startups + AI workloads Easy scaling (EC2, ECS/EKS, RDS) Strong monitoring (CloudWatch) Cost control options for MVP stage 2. Deployment Strategy Architecture (MVP → Scalable) Dockerized services (API, workers) Deploy via ECS (simpler than Kubernetes initially) NGINX as reverse proxy PostgreSQL (RDS) S3 for storage CI/CD GitHub Actions: Build → test → deploy Environment-based configs (dev/staging/prod) Security & Reliability SSL (HTTPS) Secrets via AWS Parameter Store Logging + monitoring (CloudWatch) Auto-restart + health checks 3. What I’ll Deliver Fully deployed infrastructure CI/CD pipeline Containerized backend Monitoring + logging setup Documentation + handover session 4. Experience Deployed full-stack apps on AWS (Docker + CI/CD) Built scalable systems for automation/AI workflows Focus on clean, maintainable DevOps setups 5. Timeline ⏱️ MVP deployment: 4–7 days Key Strength I don’t overcomplicate early-stage systems—simple, scalable, cost-efficient setup first, then evolve. If you share your current backend structure, I’ll map the exact architecture before starting. I can begin immediately.

@singhashishb2

Hello,I can help deploy your Customer Auto-Triage AI system into a secure and scalable production environment. I have experience with AWS, Docker, CI/CD, API deployments, databases, monitoring, and startup MVP infrastructure. I recommend AWS because it offers strong scalability, security, managed services, and cost-effective growth from MVP to production. Similar work completed: Deployed Docker-based backend systems with FastAPI/Django, PostgreSQL, Redis, SSL, NGINX, CI/CD pipelines, and monitoring for live platforms. Deployment strategy: Docker containers, NGINX reverse proxy, managed database, GitHub Actions CI/CD, secure environment setup, backups, and monitoring. Estimated MVP deployment time: 2 to 5 days depending on project readiness. Deliverables: • Production deployment • CI/CD setup • SSL & security • Monitoring & logs • Documentation • Handover support I can start immediately and help build a reliable long-term cloud setup for your platform.

@toqeer74

Your requirement for a highly reliable cloud infrastructure tailored for an AI-driven Customer Auto-Triage System highlights the necessity of a robust CI/CD pipeline and microservices architecture. I recommend leveraging AWS, given its mature ecosystem for AI services, seamless integration for container orchestration via EKS, and excellent support for auto-scaling capabilities. I've executed similar rollouts that encompass deploying backend APIs with Docker, ensuring efficient environment management, and implementing security measures like SSL. An initial deliverable can be achieved in 30 days. Want me to sketch a quick action plan so you can see the approach?

@nathanholmes

Designing cloud infrastructure for an AI auto-triage system at MVP stage isn’t just about getting it live—the key challenge is keeping it simple enough for your team to manage while still being scalable and reliable as usage grows. I’d recommend starting on AWS with a Docker-based setup using ECS or a lightweight container service to avoid early Kubernetes complexity. CI/CD can run through GitHub Actions, with NGINX for routing, managed PostgreSQL for reliability, and secure environment handling via secrets management. This keeps deployment clean, cost-effective, and easy to expand later into autoscaling or microservices. I’ve worked with structured deployments and automation-focused systems, so I’m comfortable helping early-stage teams move from partial backend to production-ready infrastructure. Happy to outline a quick architecture or MVP rollout timeline if helpful.

@faisalansari4698

I’ve reviewed your requirements and this aligns very closely with my experience as a Cloud & DevOps Architect. I can help you design, deploy, and optimize a scalable, secure, and production-ready infrastructure. ? What I bring to your project: Deployment & Containerization End-to-end setup using Docker with optimized container builds Kubernetes (AKS/EKS/GKE) setup for orchestration (if needed) Production-grade deployment strategies (rolling updates, blue-green) CI/CD Automation Robust pipelines using GitHub Actions / GitLab CI / Jenkins Automated build, test, and deployment workflows

@Manikantasaini

Hi, Your Customer Auto-Triage System is an exciting AI-driven project, and I’d love to help you deploy it into a scalable production environment. I specialize in AWS-based cloud infrastructure, Dockerized deployments, and CI/CD pipelines. For your system, I recommend AWS due to its mature ecosystem, scalability, and strong support for microservices (ECS/EKS, S3, RDS, IAM). It’s ideal for startups needing flexibility, reliability, and cost control. Deployment Strategy: Containerize backend services using Docker Deploy via ECS or EKS (based on complexity) Use NGINX as reverse proxy with SSL Set up CI/CD using GitHub Actions or Jenkins Configure secure environment variables (AWS Secrets Manager) Implement monitoring via CloudWatch Relevant Experience: I have worked on deploying containerized web apps with CI/CD pipelines, including API services with automated builds, secure configs, and zero-downtime updates. Timeline: MVP deployment can be completed in 5–7 days, including setup, deployment, testing, and documentation. I also provide clear documentation and a knowledge transfer session so your team can manage the system confidently. Let’s connect and bring your system to production. Best regards, Manikanta Saini PH : +91 9916220127 manikantasaini07@

@indreshpnk

Hello, I understand you need a Cloud Infrastructure & Deployment Engineer for your Customer Auto-Triage System (AI project) to design scalable cloud architecture, deployment pipeline, and production-ready infrastructure. The goal is to build a reliable, scalable and secure system for smooth AI workflow deployment. Here’s what I can provide: * End-to-end cloud architecture setup on AWS/GCP/Azure with scalable compute, storage and networking * Docker-based containerization with CI/CD pipelines (GitHub Actions/Jenkins) for automated deployments * Secure production deployment with monitoring, logging, SSL, load balancing and cost optimization I bring over 4+ years of experience in Cloud Computing, DevOps and backend deployments, working on scalable APIs, microservices systems, and production-grade infrastructure with strong focus on reliability, performance and security. Just to clarify a few things: * Which cloud platform are you currently leaning towards or should I suggest the best fit for your architecture? * Do you already have backend services containerized or should I handle Docker setup from scratch? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@yaalharini

Hi, You’re building a customer auto-triage system and need a scalable, reliable cloud setup with automated deployments—I can help you set this up cleanly and efficiently. I have 3+ years of experience in infrastructure and DevOps (Azure, Linux, VMware), working with Terraform, Docker, and CI/CD pipelines, along with L3 support for production systems. Cloud recommendation I recommend AWS for its scalability, managed services, and startup-friendly ecosystem (can also work with Azure if preferred). Relevant work Terraform-based infrastructure automation CI/CD pipelines for deployment Docker-based application setups Deployment approach Dockerize services Terraform for infrastructure CI/CD pipeline (build → test → deploy) Secure env/secrets + basic monitoring Timeline ~1–1.5 weeks for MVP Quick question: Do you already have backend services ready, or should the setup support development as well? Rate: 1050rs(flexible)

@itsvinaygarg4

Experienced DevOps engineer specializing in AWS/GCP, Docker, Kubernetes, and CI/CD. I can design, deploy, and scale your AI auto-triage system with secure, reliable, and production-ready cloud infrastructure, including monitoring, cost optimization, and full documentation.

@simma0501

Your AI auto-triage MVP needs a secure AWS foundation that can move from development to production without overengineering.I recommend AWS for its scalability, flexibility, and cost-efficiency for an MVP AI system. Start with EC2, RDS, and S3, and scale to containers or microservices as needed with strong DevOps and security support SCAIMLON LABS is a Chennai & Bengaluru-based Cloud MSP and certified Microsoft Azure, AWS & GCP Partner You need production-ready cloud infrastructure for an AI-based customer auto-triage system, where backend APIs can be deployed securely, monitored properly, and scaled as the MVP grows. The setup must be simple enough for a startup team to maintain after handover We will deploy a lean AWS production setup for your AI auto-triage MVP using EC2, RDS/PostgreSQL, S3, VPC, IAM, Security Groups, Route 53, ACM SSL, CloudWatch, Docker, NGINX, and GitHub Actions, and deliver secure API hosting, CI/CD, monitoring, documentation, and knowledge transfer • AWS production-ready infrastructure for API, database, storage, and networking • Dockerized backend deployment with NGINX reverse proxy and SSL • GitHub Actions CI/CD pipeline for automated build and deployment • CloudWatch monitoring, logging, alerts, and basic performance checks For FirePack Private Limited, we delivered cloud deployment readiness with hosting structure, environment setup, release flow, and handover documentation.

@saadhey

Hi! With 6 years of cloud infrastructure and DevOps experience deploying AI and backend systems, I can get your Customer Auto-Triage System into a reliable production environment without overengineering it for where you are right now. For an MVP at your stage I would recommend AWS because the free tier gives you meaningful runway, ECS with Docker handles your containers cleanly without needing the full complexity of Kubernetes yet, and the ecosystem around RDS, S3 and CloudWatch covers your database, storage and monitoring needs in one place. I will set up the full deployment pipeline including Docker containerization, GitHub Actions CI/CD, NGINX reverse proxy, SSL configuration, secrets management and logging so your team has a production grade foundation they can actually maintain going forward. Knowledge transfer session and documentation included. Realistic timeline for a clean MVP deployment is 7 to 10 days once your backend services are ready to hand over. Could you share what is currently built on the backend so I can assess the deployment complexity accurately?

@RobeelHassan

I’ve helped teams take AI MVPs into production, so I can guide you from setup to a stable, scalable deployment without overcomplicating things. Cloud choice: I’d recommend AWS — mainly for its flexibility, mature ecosystem, and easy scaling (ECS/EKS, RDS, S3, etc.). It’s also very startup-friendly in terms of cost control and growth. Approach: • Containerize your backend using Docker • Deploy via ECS (simpler) or Kubernetes if you want future scalability • Set up CI/CD (GitHub Actions) for auto-deployments • Configure NGINX, SSL, and secure secrets management • Add monitoring/logging and basic autoscaling I’ve done similar setups where AI-based services were deployed with API layers, databases, and auto-scaling, ensuring smooth performance under load. Timeline: ~1–2 weeks for MVP deployment (depending on current readiness) I’ll also provide clear documentation + handover session so your team can manage things confidently.

@Abdulraheem3000

Hi, I can help deploy and scale your AI-based Customer Auto-Triage System using a secure and production-ready cloud setup. I recommend AWS for this MVP because of its strong ecosystem, scalability, managed services, and cost-efficient startup deployment options. I can set up Dockerized backend deployment, CI/CD pipelines, NGINX reverse proxy, SSL, monitoring, and database hosting with proper security practices. I have experience deploying backend APIs, cloud infrastructure, and automation workflows using Docker, GitHub Actions, PostgreSQL/MongoDB, and AWS services. Estimated MVP deployment timeline: 4–7 days depending on infrastructure complexity and backend readiness. I can also provide complete documentation and a knowledge transfer session for your team. Thanks