Ralabs

We are seeking a talented Senior DevOps Engineer to join our dynamic team.

🕐 Engagement: Part-time, approximately 4 hours per day

📍 Polygraph: On-site in Kyiv

🏠 Work format: Fully remote
 

What is this project about?
An analytics platform for the defense domain that uses artificial intelligence to analyze data from multiple sources, generating meaningful insights and recommendations. By automating data analysis and highlighting key findings, the platform helps organizations make faster, more informed decisions, reduce manual effort, and improve overall business performance.
 

What is the team size and structure?
Project Manager, Architect, ML Engineers, UI/UX Designer, Business Analyst, Backend Engineers, DevOps.
 

What is the interview process like?
Recruiter interview — up to 30 minutes;
Technical interview — up to 1.5 hours;
Polygraph screening (on-site in Kyiv).
 

Requirements:

● 4+ years of hands-on experience in DevOps or infrastructure engineering;
● Strong experience with on-premise or hybrid infrastructure setup, including Linux OS configuration, networking, storage, and environment preparation;
● Experience with containerization and deployment tooling, including Docker, container runtime configuration, and service deployment;
● Strong experience building and maintaining CI/CD pipelines for development, testing, and technical validation environments;
● Understanding of AI/ML infrastructure needs, including inference serving setup, GPU-based workloads, model-serving environments, and evaluation harness support;
● Experience with monitoring, logging, access control, secrets management, and troubleshooting of infrastructure and deployment issues;
● Intermediate level of English;
 

Responsibilities:
● Configure base infrastructure for the first phase, including Linux OS setup, networking, storage, containerization, and required development environments;
● Set up and maintain CI/CD pipelines to support development, deployment, and validation activities;
● Support inference serving stack setup, including deployment environment preparation for model serving and evaluation harness;
● Ensure infrastructure stability, access control, secrets management, logging, and basic monitoring for technical validation environments;
● Collaborate with architects, backend engineers, and ML engineers to identify infrastructure needs, deployment constraints, risks, and required improvements;