AI Network Engineer

We are building software for modern platforms and operating systems, supporting leading networking equipment manufacturers, cloud-native solutions, and infrastructure projects. AI/ML is a core tool in addressing complex engineering and networking challenges.

• Hands-on experience with LLM-driven workflows, agentic frameworks such as LangChain and LangGraph, and tool-calling patterns
• Experience designing structured tools with clear descriptions, input/output schemas, validation logic, and integration with external APIs or command-based systems
• Experience with semantic search, vector databases, RAG patterns, prompt engineering, and structured LLM outputs
• Experience creating golden queries, automated tests, regression checks, and chatbot/agent response evaluations including LLM-as-a-judge approaches
• Proven experience developing production-quality Python code, including automated tests and maintainable integration logic
• CCNA certification or equivalent knowledge with understanding of networking platforms, device commands, and troubleshooting
• English language proficiency at B2 level minimum, preferably C1 or C2

Nice to have:

• Experience with AI-assisted coding tools such as GitHub Copilot, Codex, or Cursor
• Familiarity with Model Context Protocol, MCP server design, tool discovery, tool permissions/scopes, and agent-to-agent communication patterns
• Understanding of routing agents, supervisor/planner patterns, multi-agent workflows, guardrails, and hybrid architectures combining deterministic logic with LLM-based reasoning
• Experience with LLM evaluation platforms such as DeepEval, OpenAI Evals, LangSmith, TruLens, BenchLLM
• Practical knowledge of classification, clustering, anomaly detection, time-series analysis, or statistical methods applied to telemetry and operational data
• Experience deploying AI/LLM-based solutions with Docker, Kubernetes, CI/CD, monitoring, MLOps, or cloud/hybrid infrastructure
• Experience building interactive analysis tools and visualizations using Jupyter, Streamlit, Plotly, Altair, matplotlib

• Developing MCP-like tools that expose network device APIs and CLI commands with clear descriptions, structured inputs/outputs, validation logic, and error handling
• Managing tool metadata and supporting semantic search over available tools using a vector database
• Creating golden user queries, expected answers, and query variations for specific tools, intents, and network-operation scenarios
• Building automated tests to verify correct tool selection, tool parameterization, output structure, and end-to-end agent responses
• Designing evaluation workflows combining deterministic checks, human review, and LLM-as-a-judge techniques using tools like DeepEval or custom evaluation prompts
• Refining prompts, tool descriptions, schemas, and agent workflows while monitoring regressions when new tools or changes are introduced
• Developing production-quality Python code and tests using frameworks such as LangChain and LangGraph
• Collaborating with software engineers, network domain experts, and DevOps teams to deliver reliable, testable, and maintainable agentic workflows

English proficiency at B2 minimum is required