DevOps Engineer

We successfully deliver an all-in-one tool - Synerise (YouTube link). An ever-evolving behavioral data platform, enhanced by AI to generate outstanding ROI in more than 30 markets for industry leaders in Retail, Banking, eCommerce, Automotive, Insurance, and Telco, processing more than 150 billion transactions annually. However, we don't limit ourselves solely to this solution. We're building BaseModel (website link) - a foundation model for behavioral data embedded within a novel platform for enterprise Data Science teams, that is another step on our path to create innovation in AI and demonstrate its potential for the business world.

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Having such great solutions, we are looking for a highly motivated Devops Engineer o join our Technology Department.

• Maintain and develop production infrastructure for a large-scale, real-time multi-tenant platform.
• Work closely with backend engineers to support reliability, scalability, and availability of services.
• Implement and improve automation for deployments, infrastructure provisioning, and operational tasks.
• Participate in incident handling, root cause analysis, and post-incident improvements.
• Take responsibility of designing new infrastructure solutions and initiatives.

• Experience working with production systems in cloud environments (Azure preferred).
• Good understanding of Linux systems and basic networking concepts.
• Familiarity with CI/CD pipelines and Infrastructure as Code tools.
• Hands-on experience with Kubernetes or container-based platforms.
• Willingness to take responsibility for the quality and stability of systems you work on.
• Practical, problem-solving mindset and openness to learning new technologies.

• Exposure to data-heavy or event-driven systems (e.g. Kafka, multi-terabyte databases).
• Basic knowledge of monitoring, alerting, and observability practices and tooling.
• Interest in system performance, reliability, or cloud cost efficiency.
• Awareness of security considerations in cloud and infrastructure environments.
• Exposure to infrastructure handling large traffic volumes and data throughput.

• Work on a production-grade product used by enterprise customers in the AI and big data domain.
• Opportunity to grow technically while working with real-world, distributed systems at scale.
• Support from experienced engineers and space to take on more responsibility over time.
• Influence on day-to-day technical decisions within your team.
• Fully remote work model.