AI Network Engineer

• Flexible working hours and approach to work: fully remotely, in the office or hybrid

• Professional growth supported by internal training sessions and a training budget

• Solid onboarding with a hands-on approach to give you an easy start

• A great atmosphere among professionals who are passionate about their work

• The ability to change the project you work on

• AI and development expertise: Hands-on experience with LLM-driven workflows, agentic frameworks such as LangChain and LangGraph, and tool-calling patterns

• Agentic tool development: Experience designing structured tools with clear descriptions, input/output schemas, validation logic, and integration with external APIs or command-based systems

• Search, RAG, and prompting: Experience with semantic search, vector databases, RAG patterns, prompt engineering, and structured LLM outputs

• Testing and evaluation: Experience creating golden queries, automated tests, regression checks, and chatbot/agent response evaluations, including LLM-as-a-judge approaches

• Python engineering: Proven experience developing production-quality Python code, including automated tests and maintainable integration logic

• Networking expertise: CCNA certificate or equivalent knowledge. Understanding of networking platforms, device commands, and troubleshooting

• English (B2 level at minimum, but preferably C1 or C2)

Beyond the criteria above, we would appreciate the nice-to-haves:

• Experience with AI-assisted coding tools such as Codex, GitHub Copilot, Cursor, or similar is a plus

• MCP and agent interoperability: Familiarity with Model Context Protocol, MCP server design, tool discovery, tool permissions/scopes, and emerging agent-to-agent communication patterns such as A2A

• Advanced agent architectures: Understanding of routing agents, supervisor/planner patterns, multi-agent workflows, guardrails, and architectures combining deterministic logic with LLM-based reasoning

• LLM evaluation tooling: Experience with frameworks and platforms such as DeepEval, LangSmith, OpenAI Evals, TruLens, BenchLLM, or similar tools for evaluating LLM and agent workflows

• AI/ML for infrastructure data: Practical knowledge of classification, clustering, anomaly detection, time-series analysis, or statistical methods applied to telemetry, syslog, events, alerts, or operational data

• Production deployment and operations: Experience deploying AI/LLM-based solutions in production environments, including Docker, Kubernetes, CI/CD, monitoring, MLOps, or cloud/hybrid infrastructure

• Interactive analysis and visualization: Experience building dashboards, notebooks, or lightweight applications for analysis and validation using tools such as Jupyter, Streamlit, Plotly, Altair, matplotlib, or similar

Responsibilities

• 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, for example using 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