Must have

• Git

• Linux

• Docker

• Kubernetes

• AWS

• Machine Learning

• MLOps

• Python

• Bash script

• English (C1)

Requirements description

• 4+ years of experience with Python programming and Bash scripting
• Hands-on experience with Docker & Kubernetes
• Proven ability to design and implement CI/CD pipelines (e.g. GitLab CI, Argo CD)
• Strong background in infrastructure automation using tools like Ansible, Terraform, and AWS CloudFormation
• Proficiency with cloud platforms: AWS, GCP, Azure
• Experience with managed Kubernetes services (EKS, GKE, AKS)
• Familiarity with monitoring, logging, and alerting tools
• 1+ year of experience in Machine Learning domains (e.g. Computer Vision, NLP, Predictive Modelling)
• Experience in productionizing ML solutions
• Hands-on with MLOps tools such as MLflow, Kubeflow, or Amazon SageMaker

Offer description

We are looking for a skilled MLOps Engineer with a strong background in cloud infrastructure, automation, CI/CD, and hands-on experience with machine learning deployment. You will work at the intersection of data science and engineering, helping to bring ML models to production and ensure their scalability, reliability, and maintainability within cloud-native environments.

This role is ideal for someone who thrives in modern, cloud-based ecosystems, understands ML lifecycle challenges, and is passionate about automating infrastructure and model deployment pipelines.

Your responsibilities

1. Design, implement, and maintain CI/CD pipelines for both application and ML workflows (GitLab CI, Argo CD)
2. Develop infrastructure as code using tools like Terraform, Ansible, and CloudFormation
3. Automate and manage cloud-native environments (AWS, GCP, Azure) and Kubernetes clusters (EKS, GKE, AKS)
4. Deploy and monitor machine learning models in production using MLflow, Kubeflow, or SageMaker
5. Collaborate with data scientists and software engineers to operationalize ML solutions
6. Ensure observability with modern monitoring, logging, and alerting tools

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