JN
$155,500 – $315,000
Listed on Juniper Networks’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role develops and deploys AI agent systems that autonomously diagnose and resolve network problems using large language models and production-grade ML infrastructure. It suits experienced data scientists comfortable shipping reliable AI systems in cloud environments who want to work at the intersection of generative AI research and operational software engineering.
Our summary, not Juniper Networks’s wording. The full posting is on their site.
Skills this role names
- Anomaly Detection
- ArgoCD
- CI/CD
- Docker
- FastAPI
- GitHub Actions
- GraphQL
- Jenkins
- Kubernetes
- LangChain
- LangGraph
- Live Streaming
- Model Context Protocol
- Prometheus
- Prompt Engineering
- Python
- React
- REST API
- Retrieval-Augmented Generation (RAG)
- Time Series Analysis
- Tool Calling
- Vector Databases
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What they ask for
Required
- Master's or PhD in computer science, data science, mathematics, statistics, or related quantitative field
- 4-6 years building production ML/AI systems
- 1-2 years hands-on with generative AI and LLM applications
- Production experience with agentic orchestration frameworks (LangGraph preferred)
- Understanding of agentic design patterns including ReACT, tool calling, and multi-step planning
- Hands-on MCP or equivalent tool-serving protocol experience
- LLM API integration at scale including prompt engineering and structured outputs
- RAG pipeline design and implementation
- Evaluation frameworks for non-deterministic AI systems
- Strong statistical and machine learning foundation
- Applied ML intuition for production debugging
- Proficient Python in production codebases
- Kubernetes deployment and management
- CI/CD pipeline experience
- Docker container image management
- Backend service development with FastAPI or equivalent
- Observability for AI systems
Nice to have
- Agent memory systems experience (LangMem or custom architectures)
- Sandboxed code execution environments (E2B, Firecracker)
- Networking domain knowledge in wireless/wired diagnostics
- AWS Bedrock or OpenSearch Serverless experience
- Strong written and verbal communication skills