MLOps Engineer

Location: Remote

Engagement: Full-time, long-term

About the Role

We are looking for a skilled DevOps/MLOps Engineer to join a global project focused on AI and Machine Learning productisation. You will work closely with Data Science teams to ensure seamless deployment, scalability, and monitoring of ML solutions in production environments.

Key Responsibilities

• Design, implement, and maintain CI/CD pipelines (e.g. GitLab, Argo CD)

• Automate infrastructure using IaC tools (Terraform, Ansible, AWS CloudFormation)

• Build and manage cloud environments (AWS, GCP, Azure)

• Deploy and manage Kubernetes clusters (EKS, GKE, AKS)

• Implement monitoring, logging, and alerting solutions

• Collaborate with Data Science teams to productise ML models using MLOps tools (MLflow, Kubeflow, Amazon SageMaker)

• Troubleshoot and resolve complex technical issues

  
  

Must-Have Requirements

• 4+ years of experience in DevOps (Git, Linux, Bash, Python, Docker, Kubernetes)

• Strong cloud infrastructure expertise (AWS, GCP, or Azure)

• Proven experience in infrastructure automation (Terraform, Ansible, CloudFormation)

• At least 1 year of experience in ML/MLOps

• Good understanding of data science concepts (train/test datasets, overfitting, classification, clustering)

• Excellent troubleshooting skills

• English – B2 or higher

  
  

Nice to Have

• Experience with productisation of ML solutions

• Familiarity with Generative AI/LLMs

  
  

We Offer

• Remote work in an international environment

• Involvement in AI/ML-focused initiatives

• Opportunity to work with cutting-edge cloud and MLOps tools