Mlops cloud azure Aws with Kubernetes and fast API

We are seeking a skilled MLOps Engineer with expertise in deploying and managing machine learning models utilizing cloud-native CI/CD pipelines, FastAPI, and Kubernetes, excluding Docker. The perfect candidate will have a strong background in scalable model serving, API development, and infrastructure automation on the cloud utilizing native container alternatives or pre-built images. Responsibilities will include designing, developing, and maintaining CI/CD pipelines for ML model training, testing, and deployment on cloud platforms such as Azure, AWS, and GCP. You will be tasked with creating REST APIs using FastAPI for model inference and data services, as well as deploying and orchestrating microservices and ML workloads on Kubernetes clusters like EKS, AKS, GKE, or on-prem K8s. It will be essential to implement model monitoring, logging, and version control without Docker-based containers, utilizing alternatives such as Singularity, Buildah, or cloud-native container orchestration. Automation of deployment pipelines using tools like GitHub Actions, GitLab CI, Jenkins, and Azure DevOps will also be part of your role. Additionally, you will manage secrets, configurations, and infrastructure using Kubernetes secrets, ConfigMaps, Helm, or Kustomize, while collaborating closely with Data Scientists and Backend Engineers to integrate ML models with APIs and UIs. Your responsibilities will also include optimizing performance, scalability, and reliability of ML services in production. The ideal candidate should possess strong experience with Kubernetes, including deployment, scaling, Helm, and Kustomize. A deep understanding of CI/CD tools like Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps is required. Proficiency in FastAPI for high-performance ML/REST APIs is essential, along with experience in cloud platforms like AWS, GCP, or Azure for ML pipeline orchestration. Familiarity with non-Docker containerization or deployment tools such as Singularity, Podman, or OCI-compliant methods is preferred. Strong Python skills and familiarity with ML libraries and model serialization (e.g., Pickle, ONNX, TorchServe) are also necessary, as well as a good understanding of DevOps principles, GitOps, and IaC (Terraform or similar). Preferred qualifications include experience with Kubeflow, MLflow, or similar tools, along with familiarity with model monitoring tools like Prometheus, Grafana, or Seldon Core. An understanding of security and compliance in production ML systems is advantageous. A Bachelor's or Master's degree in Computer Science, Engineering, or a related field is preferred. This is a full-time, permanent position in the Technology, Information, and Internet industry.,