MLOps Cloud Azure AWS with Kubernetes and FastAPI

As an MLOps Engineer, your role involves designing, developing, and maintaining CI/CD pipelines for ML model training, testing, and deployment on cloud platforms such as Azure, AWS, or GCP. You will be responsible for developing REST APIs using FastAPI for model inference and data services. Additionally, you will deploy and orchestrate microservices and ML workloads on Kubernetes clusters, implementing model monitoring, logging, and version control without Docker-based containers. Your expertise will be crucial in automating deployment pipelines using tools like GitHub Actions, GitLab CI, Jenkins, or Azure DevOps. Working closely with Data Scientists and Backend Engineers, you will optimize the performance, scalability, and reliability of ML services in production. Key Responsibilities: - Design, develop, and maintain CI/CD pipelines for ML model training, testing, and deployment on cloud platforms (Azure/AWS/GCP). - Develop REST APIs using FastAPI for model inference and data services. - Deploy and orchestrate microservices and ML workloads on Kubernetes clusters (EKS, AKS, GKE, or on-prem K8s). - Implement model monitoring, logging, and version control without Docker-based containers. - Utilize alternatives such as Singularity, Buildah, or cloud-native container orchestration. - Automate deployment pipelines using tools like GitHub Actions, GitLab CI, Jenkins, Azure DevOps, etc. - Manage secrets, configurations, and infrastructure using Kubernetes secrets, ConfigMaps, Helm, or Kustomize. - Work closely with Data Scientists and Backend Engineers to integrate ML models with APIs and UIs. - Optimize performance, scalability, and reliability of ML services in production. Qualifications Required: - Strong experience with Kubernetes (deployment, scaling, Helm/Kustomize). - Deep understanding of CI/CD tools like Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps. - Experience with FastAPI for high-performance ML/REST APIs. - Proficient in cloud platforms (AWS, GCP, or Azure) for ML pipeline orchestration. - Experience with non-Docker containerization or deployment tools (e.g., Singularity, Podman, or OCI-compliant methods). - Strong Python skills and familiarity with ML libraries and model serialization (e.g., Pickle, ONNX, TorchServe). - Good understanding of DevOps principles, GitOps, and IaC (Terraform or similar). Preferred Qualifications: - Experience with Kubeflow, MLflow, or similar tools. - Familiarity with model monitoring tools like Prometheus, Grafana, or Seldon Core. - Understanding of security and compliance in production ML systems. - Bachelor's or Masters degree in Computer Science, Engineering, or related field.,