devops engineer for machine learning platforms

Innowise delivers technology projects for clients worldwide and has implemented more than 1600 cases for clients from the USA, Denmark, Germany, and other countries.

• Design, deploy, and support resilient infrastructure for machine learning platforms and data pipelines using Python and SQL;
• Implement Application Lifecycle Management for machine learning, automating training, versioning, and deployment processes;
• Ensure reliability, scalability, and high availability of MLOps infrastructure and backend services;
• Design and manage distributed compute environments across bare metal, virtual machines, and private or public cloud;
• Containerize ML services and applications using Docker and Kubernetes, orchestrating production rollouts;
• Automate infrastructure provisioning, cluster lifecycle, and configuration using Infrastructure as Code;
• Build, integrate, and maintain CI/CD pipelines with GitLab CI, GitHub Actions, and Jenkins;
• Implement observability through logging, metrics, and dashboards for overall cluster health;
• Diagnose bottlenecks, resolve node and network failures, and conduct Root Cause Analysis as part of proactive incident management.

• Proven experience managing server or cloud infrastructure, Infrastructure as Code tools, Docker, and Kubernetes for at least 250 person-days;
• At least 300 person-days designing and maintaining CI/CD solutions in production environments;
• At least 200 person-days deploying and using MLflow, Kubeflow, ClearML, or similar platforms;
• At least 150 person-days in root cause analysis and stabilizing critical systems;
• Strong Linux systems engineering background with RHEL, Rocky, or SLES;
• Proficiency in Python and Bash for automation;
• Experience with PostgreSQL, NFS, and S3-compatible object stores;
• Experience with Data Analytics and Data Analysis using Databricks;
• Strong analytical mindset and an implementation-oriented approach;
• Ability to translate business requirements into scalable technical solutions;
• Excellent cross-functional collaboration with Data Scientists, Developers, and IT operations;
• Nice to have: deep experience with HPC schedulers and integrations, bridging HPC schedulers with cloud-native platforms and configuring dynamic scaling, Go or Rust scripting, SQL and PowerShell, MPI workloads and GPU scheduling, parallel file systems, configuration management with Ansible or Puppet.

• Flexible work schedule;
• Experience working with clients worldwide;
• Financial assistance;
• Medical insurance.