FreeHire

SOFTSWISS

System Engineer/DevOps

Show

Purpose of the role:

You will be responsible for designing and evolving the internal platform that powers our Game Aggregator, enabling development teams to build, deploy, and operate services efficiently at scale. Acting as a bridge between software engineering and infrastructure, you will create reusable platform capabilities, establish engineering standards, and guide teams in adopting cloud-native best practices. Your work will improve system reliability, security, scalability, and developer experience across distributed, high-load environments.

Key responsibilities:

  • Design and maintain scalable, multi-tenant Kubernetes platforms using reusable Helm templates to enable developer self-service.

  • Own pipeline reliability, cost, and speed. Embed automated release gates, secure secret architectures, and software vulnerability scanning (SCA).

  • Build custom tools to extend platform capabilities and standardize shared environments.

  • Maintain distributed tracing, logging, and metrics systems. Guide development teams on service instrumentation and establishing SLIs/SLOs/SLAs.

  • Consult development teams on system architecture, resource optimization, and modern traffic routing patterns.

Required Experience:

  • 5+ years in a DevOps, SRE, or Platform Engineering role supporting high-load distributed systems.

  • Strong grasp of multi-cloud patterns, multi-region architectures, and cloud cost optimization.

  • Production-grade Kubernetes administration, including advanced scheduling policies, network isolation, and complex Helm templating.

  • Deep Linux administration and cross-stack troubleshooting. Experience configuring L4/L7 traffic layers (Load Balancers, Ingress, API Gateways, Service Meshes).

  • Mastery of declarative IaC and proficiency in Python, Go, or Bash.

  • Ability to independently gather requirements, set delivery goals, and build maintainable solutions.

  • Intermediate or higher English and Russian (B1+).

Nice to have:

  • Practical experience scaling and maintaining infrastructure for AI-powered applications, LLM frameworks, or MLOps pipelines.