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
Apply Now
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
Apply Now