MLOps / Cloud Engineer

About the position

We are looking for an experienced MLOps / Cloud Engineer with a strong background in building and operating cloud-based AI/ML platforms in production environments. The role focuses on designing scalable infrastructure, enabling end-to-end ML workflows, and supporting modern GenAI/LLM solutions. Start Date: ASAP Location: Remote (EU-based) Language: English Contract Type: B2B Responsibilities: Design, build, and operate cloud-based AI/ML platforms in production environments Develop and maintain scalable MLOps pipelines for end-to-end ML workflows Implement and optimize CI/CD pipelines for ML and software delivery (e.g., GitHub Actions) Manage and provision infrastructure using Infrastructure as Code (Terraform) Deploy, manage, and optimize containerized applications using Docker and Kubernetes (EKS) Work with AWS and Azure services, including ML services (e.g., SageMaker, Bedrock) Implement monitoring, logging, and alerting solutions (Prometheus, Grafana, Loki, ELK) Ensure security best practices across cloud infrastructure and CI/CD pipelines Support model lifecycle management including model registry, performance monitoring, and data quality tracking Collaborate with cross-functional teams to deliver robust and scalable AI/ML solutions Analyze existing codebases and suggest improvements and refactoring where needed Requirements: Hands-on experience with AWS and/or Azure cloud platforms Proven experience with Kubernetes and Docker in production environments Strong knowledge of Terraform (Infrastructure as Code) Experience with CI/CD pipelines (e.g., GitHub Actions) Proficiency in Python and solid understanding of software engineering principles and architecture Experience with LLM / GenAI solutions and ML platforms (e.g., SageMaker, Bedrock) Strong understanding of ML concepts and algorithms , with practical implementation experience Experience with MLOps tooling and architecture (e.g., Kubeflow, model registry, monitoring) Knowledge of monitoring and logging tools (Prometheus, Grafana, Loki, ELK) Understanding of security best practices in cloud and DevOps environments Nice to Have: Experience with enterprise-scale projects and environments Familiarity with advanced Kubernetes features (e.g., operators) Experience with performance optimization of Docker images Exposure to tools like Dynatrace

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