MLOps Cloud Engineer

Prague - Czech Republic

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

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

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 ...

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