I will build your mlops pipeline and ai infrastructure on aws kubernetes

Struggling to get your ML models into production? I will build a robust, scalable MLOps pipeline and AI infrastructure on AWS Kubernetes that automates your entire machine learning lifecycle.

With 5+ years in DevOps, cloud infrastructure, and ML engineering, I build production-grade MLOps systems that make AI deployment fast, reliable, and repeatable.

What I deliver:

- CI/CD pipelines for ML models using GitHub Actions and ArgoCD

- Kubernetes (EKS) clusters with auto-scaling for ML workloads

- Kubeflow pipelines for automated model training and serving

- MLflow for experiment tracking, model registry, and versioning

- AWS SageMaker integration for managed training and inference

- Feature stores (Feast) for real-time and batch feature serving

- Model monitoring with Prometheus, Grafana, and drift detection

- Infrastructure as Code using Terraform and Helm charts

- Docker containerization for reproducible ML environments

- A/B testing and canary deployments for safe model rolls

Tech stack:

AWS EKS, SageMaker | Kubeflow | MLflow | TensorFlow | PyTorch | Feast | Terraform | Helm | Docker | ArgoCD

Why choose me?

- Production-proven MLOps patterns

- Clean, documented code

- Ongoing