As the global leader in high-speed connectivity, Ciena is seeking a Generative AI Prompt Engineer to bridge the gap between networking expertise and Large Language Models (LLMs) to autonomously troubleshoot complex issues across Ciena's portfolio.
Requirements
- Deep understanding of networking fundamentals, including TCP/IP, Ethernet, and network management protocols (SNMP, gNMI/gRPC).
- Solid experience with either: Optical Networking: Concepts like DWDM, OTN, coherent optics, and familiarity with optical transport systems.
- Solid experience with either: Packet Networking: Advanced routing protocols (BGP, OSPF, IS-IS), MPLS, and Segment Routing.
- 1-2+ years of demonstrable, hands-on experience working with Large Language Models (e.g., Gemini, GPT-4, Llama series) and a strong passion for generative AI.
- Proven ability in prompt engineering, including experience with few-shot/zero-shot prompting, context structuring, and managing conversational flow.
- Basic proficiency in a scripting language, preferably Python, for testing and automation tasks.
- Deep understanding of agentic reasoning architectures, especially the ReAct (Reason+Act) framework for tool use, and broader planning models like MCP (Model-Capabilities-Plan).
Responsibilities
- Prompt Design and Development: Design, develop, test, and continuously refine a comprehensive library of prompts and prompt chains for network troubleshooting scenarios. This includes fault isolation, alarm correlation, log analysis, configuration validation, and performance degradation analysis.
- Domain Knowledge Translation: Collaborate closely with Ciena's Subject Matter Experts (SMEs), including Tier 3/4 Technical Support Engineers, Network Architects, and System Engineers, to deconstruct their expert troubleshooting methodologies into structured, machine-interpretable instructions and queries.
- Agentic Loop Design: Design and implement the core reasoning loops for our AI agents. You will build ReAct (Reason+Act) style workflows that enable the agent to form a hypothesis, execute a diagnostic command, observe the output, and then decide on the next best action, closely mimicking an expert's iterative troubleshooting process.
- Contextual Engineering: Develop sophisticated prompts that leverage Retrieval-Augmented Generation (RAG) techniques, enabling the LLM to query and reason over Ciena's vast knowledge base of product documentation, technical notes, and historical case data.
- Performance Analysis and Optimization: Establish key performance indicators (KPIs) to measure prompt and agent effectiveness, such as diagnostic accuracy, resolution time reduction, and clarity of AI-generated responses. Analyze agent behavior to identify failures in reasoning or collaboration, and iteratively improve the underlying logic.
- Stay at the Forefront: Continuously research and experiment with the latest prompt engineering techniques and architectures for AI agents to ensure Ciena's tools remain state-of-the-art.
Other
- A Bachelor’s degree in Computer Science, Electrical Engineering, Network Engineering, or a related field, or equivalent practical experience.
- 5+ years of experience in a technical networking role such as Network Operations Center (NOC) Engineer, Technical Support Engineer, Network Architect, or similar.
- Exceptional analytical and problem-solving skills, with an ability to think logically and translate complex processes into simple, clear instructions.
- Direct experience troubleshooting Ciena's hardware or software portfolio (e.g., 6500 Family, Waveserver, 8100/5100 Series, Blue Planet).
- Understanding of collaborative AI, including multi-agent systems (MAS) and communication protocols like Google's Agent-to-Agent (A2A) framework, for solving complex, multi-domain problems.
