MLOps / Cloud Engineer

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