[Hiring] Junior AI Engineer @Virtuozzo

Role Description

At Virtuozzo, the AI Engineer focuses on applying AI to improve software engineering, DevOps, CI/CD, operational workflows, and engineering productivity across large-scale infrastructure and platform environments. This role is centered on internal engineering effectiveness. The primary goal is to reduce operational overhead, improve reliability, accelerate delivery, and increase engineering efficiency through practical AI systems and automation.

The team may expose some capabilities as shared internal services or reusable platform components, but the core focus remains engineering operations and software delivery systems.

Key Responsibilities

• Engineering Productivity
  • Build AI-assisted tooling for engineering workflows
  • Improve developer productivity and operational efficiency
  • Design systems that reduce repetitive manual engineering work
  • Support large-scale multi-component software delivery environments
• CI/CD & DevOps Intelligence
  • Develop AI-driven solutions for:
    • pipeline analysis
    • failure classification
    • flaky test detection
    • release validation
    • dependency impact analysis
    • build optimization
    • deployment risk analysis
  • Improve reliability and visibility of engineering pipelines
• Operational & Engineering Automation
  • Build AI systems for:
    • troubleshooting assistance
    • incident investigation
    • log analysis
    • engineering knowledge retrieval
    • operational recommendations
  • Automate engineering support and operational workflows
• LLM & Agent Systems
  • Design and implement:
    • engineering copilots
    • RAG systems, context management systems
    • internal engineering assistants
    • workflow automation agents
  • Integrate AI with engineering systems, repositories, CI/CD platforms, documentation, observability tools, and operational data sources
• Infrastructure & Reliability
  • Deploy and maintain production-grade AI services
  • Ensure observability, monitoring, evaluation, and operational reliability
  • Optimize AI systems for latency, cost, and scalability
  • Maintain secure and maintainable integrations

Qualifications

• Engineering
  • Strong software engineering fundamentals
  • Strong Python skills
  • Experience building backend systems, APIs, and automation tooling
  • Experience with distributed systems and Linux environments
• AI & Automation
  • Practical experience with LLM systems and AI tooling
  • Experience with:
    • RAG architectures
    • embeddings
    • vector search
    • workflow orchestration
    • AI evaluation
  • Ability to build production systems, not just prototypes
• DevOps & Infrastructure
  • Experience with CI/CD systems
  • Familiarity with:
    • Kubernetes
    • containers
    • observability tooling
    • infrastructure automation
    • cloud-native environments
  • Understanding of operational workflows and engineering lifecycle challenges

Nice-to-Have

• Experience with OpenStack ecosystems
• Experience with large-scale monorepo or multi-repository environments
• Experience with engineering analytics and developer productivity metrics
• Familiarity with infrastructure observability and incident management systems

What Success Looks Like

• Reduced engineering operational overhead
• Faster issue investigation and troubleshooting
• Improved CI/CD reliability and visibility
• Higher engineering efficiency and delivery velocity
• AI systems adopted in daily engineering workflows
• Measurable reduction in repetitive manual work

Benefits

• Flexible hours and remote work options
• Competitive compensation with different benefits depending on your location and type of contract
• Recognition programs
• Space for creativity and experimentation within the company’s goals
• Supportive, engineering-driven culture with minimal bureaucracy
• The chance to influence infrastructure decisions from day one
• A smart, friendly team that values reliability, simplicity, and automation