Muhammad U.

Struggling with broken pipelines, messy Docker setups, or ML models that never make it to production? I fix that. 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.