The aim of this project is to build practical AI‑driven engineering tools. The student will work with MCP servers and agentic AI to automate repetitive tasks, improve developer workflows, and integrate intelligent assistants into our core products with focus on creating a set of tools that offer reliable and predictable results. - New tools, prompts, and resources for the existing vPB MCP server - Create MCP servers for KVO / 5G SBI / Robot - Integration of these servers with JIRA / Bitbucket / Confluence / Jenkins - Adding performance metrics for agents to evaluate and improve their behavior based on concrete data
Problems we want to solve: 1. Fix bugs, implement low‑complexity and repetitive tasks, and generate tests for new features. We will use the MCP tools to automate parts of the testing workflow: generate packet captures using Python and Scapy, configure vPB with newly implemented options, and run validation tests. Afterwards, they can instruct the AI to automatically generate a corresponding Robot Framework test for QA regressions.
Improve automation test coverage and readability
- Use AI/LLMs to generate descriptions for existing automation tests (new regression), based on test files and logs.
- Prompt‑based test generation for new regression suites.
Generate technical documentation for products. Start from feature specifications and, with input from Development Engineers, write technical documentation based on the actual implementation.
What you will gain:
• MCP architectures / agentic AI • Knowledge of our products (vPB, 5G SBI, KVO, etc.) • API integration • Exposure to DevOps tooling and CI/CD pipelines • Learn Virtualization and Kubernetes concepts
Skills required: Python, Linux, REST API
