You will get llmops pipelines and deploy large language models
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Project details
Are you looking to deploy and scale large language models (LLMs) with optimized performance? We specialize in LLM deployment, VLLM-based inference, and LLMOps pipelines to help you run your models efficiently in production.
We can help you:
• Deploy small, medium, or large LLMs on cloud or local GPU/CPU
• Optimize inference using batching, quantization, and VLLM
• Setup APIs, web services, and microservices for LLM access
• Build scalable LLMOps pipelines for training, fine-tuning, monitoring, and logging
Whether you are a startup, researcher, or enterprise, I ensure your LLM deployment is secure, efficient, and production-ready.
𝗗𝗠 𝗺𝗲 𝗳𝗼𝗿 𝗼𝘂𝗿 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼, 𝗰𝘂𝘀𝘁𝗼𝗺 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀, 𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗟𝗟𝗠𝗢𝗽𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴.
We can help you:
• Deploy small, medium, or large LLMs on cloud or local GPU/CPU
• Optimize inference using batching, quantization, and VLLM
• Setup APIs, web services, and microservices for LLM access
• Build scalable LLMOps pipelines for training, fine-tuning, monitoring, and logging
Whether you are a startup, researcher, or enterprise, I ensure your LLM deployment is secure, efficient, and production-ready.
𝗗𝗠 𝗺𝗲 𝗳𝗼𝗿 𝗼𝘂𝗿 𝗽𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼, 𝗰𝘂𝘀𝘁𝗼𝗺 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀, 𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗟𝗟𝗠𝗢𝗽𝘀 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗮𝗻𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴.
AI Development Type
Deep Learning, Knowledge Representation, Model Tuning, Recommendation System, Software MaintenanceAI Tools
Amazon SageMaker, Apache MXNet, Azure Machine Learning, deeplearn.js, Google AutoML, Keras, MATLAB, MLflow, NVIDIA AI Platform, Open Neural Network ExchangeAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$100
|
Standard
$300
|
Advanced
$500
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 15 days |
Number of Revisions | 1 | 3 | 4 |
AI Model Integration | |||
Detailed Code Comments | |||
Knowledge Graph | - | ||
Model Documentation | - | ||
Ontology | - | - | |
Source Code | |||
Taxonomy | - | - |
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About Muhammad
DevOps Engineer | MLOps | Docker, Kubernetes | CI/CD | AWS
100%
Job Success
Narowal, Pakistan - 7:53 am local time
I am a DevOps and MLOps Engineer with 3+ years of experience running production AI/ML infrastructure for real products. At EmaagoTech, I managed 20+ CI/CD pipelines, cutting deployment time from 60 minutes to under 15, maintained a 21-container speech-to-text ML platform, and ran GPU-based AWS inference for underserved language models. I do not just set things up. I keep them running.
𝗪𝗵𝗮𝘁 𝗜 𝗱𝗲𝗹𝗶𝘃𝗲𝗿:
➞ CI/CD Pipelines: GitHub Actions, GitLab CI, Jenkins, built from scratch or rescued
➞ Containers: Docker, Kubernetes, Helm, ArgoCD, setup, optimization, production management
➞ MLOps: ML model deployment, GPU workloads on AWS, Kafka pipelines, Label Studio
➞ Self-Hosted Apps: n8n, Appwrite, Dify, Outline, Baserow, any Linux VPS
➞ Cloud: AWS, Azure, GCP, DigitalOcean, migration, setup, cost optimization
➞ Infrastructure as Code: Terraform, Ansible, automated provisioning
➞ Monitoring: Prometheus, Grafana, ELK Stack, CloudWatch, real-time observability
𝗪𝗵𝘆 𝗰𝗹𝗶𝗲𝗻𝘁𝘀 𝗸𝗲𝗲𝗽 𝗵𝗶𝗿𝗶𝗻𝗴 𝗺𝗲 𝗯𝗮𝗰𝗸:
➞ 100% Job Success Score with Top Rated badge on Upwork
➞ Currently 4+ months into a complex cloud engagement for a US-based client
➞ Active on a 2500-dollar AI platform deployment, I finish what I start
➞ Average response time under 2 hours, you will not be left waiting
𝗥𝗲𝗰𝗲𝗻𝘁 𝗿𝗲𝘀𝘂𝗹𝘁𝘀:
➞ Reduced Docker image build time from 2 to 3 hours to under 10 minutes
➞ Cut deployment time by 80% across 15+ production pipelines
➞ Managed 20 Label Studio containers supporting 100+ data annotators
➞ Deployed secure VPN architecture, cutting server update time by 80% for a US client
Whether you need a pipeline built today, a broken deployment fixed tonight, or a long-term infrastructure partner, send me a message.
I respond fast and can usually tell you within 10 minutes if I can solve it.
Steps for completing your project
After purchasing the project, send requirements so Muhammad can start the project.
Delivery time starts when Muhammad receives requirements from you.
Muhammad works on your project following the steps below.
Revisions may occur after the delivery date.
Step 1 - Requirement Review
Client purchases the project and shares all necessary details, goals, and assets. I confirm the requirements and clarify any questions before beginning.
Step 2 - Planning & Setup
I analyze the requirements, prepare the project structure, set up tools/environments, and finalize the delivery timeline.