MLOps engineer needed – Dockerize LLM pipeline & deploy to AWS

@ZohaibRoy

⭐⭐⭐⭐⭐ Deploy Your LLM Pipeline with Docker on AWS Today! ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for someone to containerize your LLM pipeline and deploy it on AWS. Look no further; Zohaib is here to help you! My team has handled over 50 similar projects in deploying machine learning models. I will create a production-grade Dockerfile, deploy your container on AWS, and ensure everything runs smoothly by the end of the day. ➡️ Why Me? I can efficiently handle your LLM deployment as I have 5 years of experience in containerization and AWS. My skills include Docker, AWS ECS, and networking. Additionally, I have a strong grip on CI/CD practices, ensuring a seamless deployment process. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I look forward to our conversation! ➡️ Skills & Experience: ✅ Docker ✅ AWS ECS ✅ AWS EC2 ✅ Python ✅ Containerization ✅ CI/CD ✅ Networking ✅ IAM Roles ✅ Bash Scripting ✅ Security Groups ✅ Multi-stage Dockerfiles ✅ Smoke Testing Waiting for your response! Best Regards, Zohaib

@techplusintl

Hi, ★★★ Python / Docker Expert ★★★ 8+ Years of Experience ★★★ I recently deployed a containerized LLM inference pipeline on AWS for a client using Docker + ECS Fargate, with environment-based config management, IAM roles, CloudWatch logging, and a public API endpoint behind an ALB. I can start immediately and complete your Dockerization, AWS deployment, smoke testing, and handoff documentation today without hand-holding. Comfortable with ECS, EC2, GPU-backed instances if needed, networking, and production-ready deployment practices. Thanks!

@DeveloperStation

Hi, I’ve developed multiple LLM-based products, including a Chrome extension that uses OpenAI’s API to summarize Amazon reviews. We containerized the entire pipeline with Docker and deployed it on AWS, ensuring a production-ready environment with CI/CD workflows. For your project, I can deliver a fully functional LLM pipeline with a production-grade Docker image, CI/CD setup, and a responsive endpoint. I’m also well-versed in AWS services like Lambda, EC2, and S3, and I can handle server management and security. Let’s schedule a 10-minute introductory call to discuss your project in more detail and see if I’m the right fit. I usually respond within 10 minutes. Best regards, Adil

@edwarddzi8f

Hi! I can help you with this. I've containerized and deployed a Python LLM inference pipeline on ECS Fargate with a multi-stage Dockerfile, ALB endpoint, and a clean redeploy script. Available to start today and deliver same-day. Fixed price 250 USD. Edward, 10+ years Python and AWS. Let's get it done.

@axiomswb

Subject: Production-Grade LLM Containerization & ECS Fargate Deployment Optimizing your Python LLM pipeline with a multi-stage Docker build now to ensure minimal image size and secure secret handling, while architecting a scalable AWS environment on ECS Fargate (or EC2/GPU if required) with strict IAM role isolation. As a senior full-stack and MLOps engineer with 16+ years of experience, I specialize in moving containerized ML workloads from local dev to live AWS production without "magic" or technical debt. I will deliver a clean, documented handoff—including the requested Bash redeployment script—so your team has full operational independence post-launch. Is your current Python pipeline utilizing a specific GPU-accelerated library (like CUDA/PyTorch) that requires a G-series EC2 instance, or can we target the more cost-effective ECS Fargate serverless environment?

@NayaPakistan

Hello, Let me containerize and deploy your LLM pipeline to AWS today. My approach involves creating a production-ready Dockerfile and deploying it to ECS Fargate for efficient live operation. I am available to start immediately and have successfully deployed similar containerized ML workloads to AWS. Regards, Muhammad Azeem

@Feriver

Hello, I understand you need a fast, production-grade deployment of an existing Python LLM inference pipeline, containerized with Docker and deployed to AWS (preferably ECS Fargate), exposing a live endpoint with proper environment configuration, security setup, and a clean handoff—all completed within the same day. I will containerize your pipeline using a multi-stage Docker build optimized for minimal image size and stable inference performance, configure Docker Compose for reproducibility, and deploy it to AWS ECS Fargate with proper IAM roles, security groups, and environment variable management. I will ensure the service is publicly accessible via a stable endpoint and fully operational with a verified smoke test. You will also receive a clean deployment README and a Bash script for redeployment and scaling, so your team can reproduce the setup independently without hidden steps. I’m available to start immediately and focus entirely on execution to meet your same-day requirement. Thanks, Asif

@raj00565

Leveraging over a decade of experience in system administration, network engineering, and DevOps, my team and I are uniquely qualified to deliver the MLOps solution you need. We have extensive experience designing, building, and managing scalable IT infrastructures and cloud environments – critical skillsets for your project that require running a smoothly functioning LLM pipeline on AWS. Previous projects include successfully containerizing ML/LLM workloads on AWS exactly as you require. Adding to our strength is our proficiency in modern DevOps practices and cloud automation, essential for efficient system management. It enables us to execute the tasks without hand-holding, which aligns perfectly with your desired profile. We assure you a same-day delivery well within your target time frame without compromising on the quality. To ease your future maintenance and save time on ramping up new hires, we ensure every job we undertake has comprehensive documentation that clearly outlines all of its components - including the deployment scripts and configurations for smooth replication. Contact us now to get your MLOps task accomplished effortlessly!

@toriquldev123

Dear Client, I'm an experienced full-stack developer specializing in containerized ML/LLM deployments on AWS, with over 10 years of experience. I understand you need a Python LLM pipeline Dockerized and deployed to AWS ECS Fargate with a live endpoint by end of day. I've shipped a similar project — containerized a RAG-based LLM inference pipeline using FastAPI, deployed to ECS Fargate with IAM roles and security groups, and delivered a redeployable README. My skills in Docker, ECS Fargate, EC2, and AWS networking make me confident I can deliver exceptional results. Feel free to share additional details or ask questions. I'm happy to refine my approach to fit your needs. Hello There! I'm Md Toriqul Islam, and I'm excited to partner with you & I can dive into your project immediately. I have rich experience in AWS deployment. I am skilled in Docker, ECS, and Python. I understand you want same-day execution. I'm ready to start immediately and would be happy to discuss this project. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@dvcontact

Hi, This seems related to deployment and container config rather than the model code — common blockers are missing runtime libs, large base images, or incorrect env/secret handling. I’d first build a multi-stage Dockerfile that pins Python deps, strips dev artifacts, and exposes the inference port, then validate locally with docker-compose and push the image to your registry. Next I’ll deploy to ECS Fargate (or EC2 if you need GPU), attach a minimal task IAM role and security group, and run a smoke test hitting the endpoint. I shipped a containerized LLM inference on ECS Fargate last quarter. I’m available to start now; bid: $750 for same-day delivery with Dockerfile, compose, AWS deploy, smoke test, and README. Ready to start — share repo/access. --Smith

@Tacticssoft

Hi, I recently deployed a containerized NLP inference service where a Python LLM pipeline was packaged using a multi-stage Docker build and deployed on AWS ECS Fargate with an exposed REST endpoint behind an Application Load Balancer. The setup included environment-based configuration, IAM roles, CloudWatch logging, and a redeploy-ready Bash script for one-command updates. I’m available to start immediately today and can commit to same-day delivery assuming repository access and AWS credentials are ready. My fixed-price bid is $140 for complete containerization, AWS deployment, smoke testing, and handoff documentation.

@ahmadpiran

Hey, Containerizing an LLM pipeline and getting it live on ECS Fargate same-day is a clean execution task and exactly the kind of work I do without needing ramp-up time. I deployed a containerized inference service on AWS ECS Fargate for a Python-based ML pipeline last year, multi-stage Dockerfile, Secrets Manager for credentials, ALB in front, and a redeploy script handed off to the client on the same day. I am available to start right now and will deliver a working endpoint, a production-grade Dockerfile, correct IAM roles and security groups, and a clean README so you can redeploy independently without touching me again. Fixed price: $220. Is the model being loaded from S3 or baked into the image, so I can size the Fargate task definition and plan the cold start correctly before I touch a single line? Best, Ahmad

@mayankinnovative

Hi, As per my understanding: You already have a working Python-based LLM pipeline and need immediate execution-focused support to containerize it with Docker and deploy it live on AWS within the same day. The goal is a production-ready deployment with a working endpoint, proper environment configuration, basic AWS networking/security setup, and a clean handoff process for independent redeployment. Implementation approach: I will quickly review the existing inference pipeline, create an optimized production-grade Dockerfile and Docker Compose setup, then deploy the workload on ECS Fargate or EC2 depending on GPU/runtime requirements. I will configure IAM roles, security groups, environment variables, logging, and endpoint exposure, followed by smoke testing to verify live inference responses. Finally, I will provide a concise deployment README and redeployment scripts so the infrastructure remains maintainable without undocumented steps. A few quick questions: 1. Does the current LLM pipeline require GPU acceleration or can it run fully on CPU? 2. Which AWS resources/accounts are already available for deployment? 3. Is the inference API already exposed through FastAPI, Flask, or another framework? 4. Do you need HTTPS/domain setup or only a working public endpoint for now? 5. Are model weights stored locally, S3, or pulled from Hugging Face during deployment?

@kumarashish99

I am Ashish, an AWS-certified professional with over five years of experience in backend development and DevOps engineering – Skills that would certainly come in handy for your project. Among my numerous achievements, I recently containerized a similar ML/LLM product as this, deploying it to AWS using ECS with Fargate instances; a project that has already been
christened a success. Success breeds success, and I have no doubt in my ability to deliver on your project swiftly. As I write this pitch, I am able and ready to jump into action immediately. In summary, my experience wielding AWS ECS/Fargate with Docker coupled with my skills in backend development make me the perfect candidate for this job. Not only am I capable of Huckfinn-styled execution without constant oversight but am also highly available, ready to hit the ground running today! So let's get started on transforming your MLOps system on AWS for utmost efficiency...consider me hired!

@marjanahmed13

Hi! My name is Marjan and I'm here to offer you my services as a skilled applicant with over a decade of experience working on Freelancer.com. l believe I am the best fit candidate for this project due to my extensive experience; I would like to have a discussion to get to know that we both are on the same page. Once the scope will be locked, I will start working on it right away.

@hr157

Hi, I can start immediately and handle same-day execution. I’ve previously deployed Python-based AI/LLM workloads using Docker on AWS with ECS/EC2 setups, including API-serving pipelines, GPU-compatible inference environments, environment-based secret handling, and production-ready container orchestration. I’m comfortable building optimized Dockerfiles, ECS Fargate deployments, IAM/security configuration, networking, endpoint exposure, and deployment handoff documentation without needing ramp-up time. I can provide a clean Docker + AWS deployment workflow with smoke testing and redeploy instructions by end of day. Availability: Immediate. Fixed-price bid can be finalized quickly after reviewing the current inference stack and GPU/runtime requirements. Thanks

@mickeysz

Hi I have experience in AWS with US client You can check my reviews I'm very confident in your project I can start it immediately Thank you Mickey

@sant527

I am interested in this work. I worked in python (backend) and Js/react (frontend) FastAPI / flask/ Django Google Cloud Platform (Cloud Run, Firestore, Pub/Sub, IAM) AWS worked with ECS, EC2, AWS networking GitHub Actions/Jenkins Docker & Kubernetes Multi-tenant SaaS Architecture RAG pipelines, chatbots, deployment in clound If you are interested please ping me, we can discuss more details on this

@juanpablomacf

Hi - I’m Juan Pablo, MLOps/AI engineer with hands‑on experience containerizing LLM inference pipelines and deploying them to AWS ECS Fargate and EC2 (GPU) in production. Recently shipped a similar project where I built a multi‑stage Dockerfile, optimized image size, and deployed a Python LLM service behind an AWS ALB with IAM‑based secrets and automated redeploy scripts. I can start today, deliver same‑day Dockerization + AWS deployment, and provide a clean README + smoke test. Strong background in Docker for ML and AWS ECS deployments. My fixed price for the full task is $250.