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MLOps / Cloud Engineer

Location

Worldwide

Posted

66 days ago

Salary

0

Seniority

Mid Level

Job Description

MLOps / Cloud Engineer

Whirr Crew

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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