AI Network Engineer, REMOTE

• 
  

  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

  
  

We're building a software for modern platforms and operating systems, supporting leading networking equipment manufacturers, cloud-native solutions, and infrastructure projects. AI/ML is increasingly at the heart of these initiatives—not as an add-on, but as a core tool to address complex engineering and networking challenges. We are seeking an engineer with a strong software engineering background, solid AI/ML expertise, and experience in computer networks

• 
  

  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

  
  

,[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] Requirements: AI, Testing, Python, Networking, CCNA, GitHub, Statistical methods, Docker, Kubernetes, CD, MLOps, Cloud, Matplotlib