Network Software Engineer - Observability & AI

We are seeking a Network Software Engineer to join the Network Observability team within Equinix's platform engineering organization. This role sits at the intersection of network engineering, AI-native development, and Observability platform development. You will design and build intelligent Network APIs grounded in real-time observability data, network asset intelligence, and inventory systems then expose those capabilities via Model Context Protocol (MCP), APIs, Streams and autonomous agents that power self-service experiences for Equinix customers and internal operators.

You will work closely with Network architects, product teams, platform architects, and data engineers to translate raw network telemetry into programmable, AI-consumable services. You will mentor junior engineers and set the technical direction for how network intelligence is surfaced, automated, and governed across Equinix's global infrastructure.

In this role, you will have direct ownership over a genuinely novel engineering problem: making Equinix's global network intelligence programmable by both humans and AI. Concretely, you will ship:

• AI-native Network APIs high-fidelity, low-latency APIs that expose real-time observability signals and network asset data as structured, agent-queryable surfaces
• MCP Servers for Network Capabilities production MCP implementations that let any compatible AI agent safely query network health, inspect topology, validate configurations, and trigger network self-service workflows
• Autonomous Network Agents intelligent agents that help Equinix customers and operators accomplish network tasks through natural language: diagnosing circuit issues, modeling capacity scenarios, and automating routine change workflows
• Observability-Grounded Intelligence systems that ground every AI inference in live, authoritative network data, ensuring agents operate with current topology, accurate asset inventory, and real-time health signals

• Translate complex network and observability requirements into clear API contracts and data models
• Partner with product managers and business analysts to convert network observability use cases latency anomaly detection, capacity thresholds, topology drift into actionable engineering requirements
• Evaluate feasibility across the full data stack: streaming telemetry pipelines, asset inventory systems, and AI inference layers

• Participate in and drive architectural reviews for AI-native Network API platforms
• Define reference architectures for exposing network intelligence via MCP servers, ensuring agents can safely query, reason over, and act on live network data
• Design systems that integrate observability data sources (streaming telemetry, SNMP, flow data, topology graphs) with inventory and asset repositories as a unified programmable layer
• Lead the design of AI-native Network APIs that serve both human developers and autonomous AI agents as first-class consumers
• Architect MCP tool and resource definitions that expose network capabilities topology queries, health checks, capacity lookups, change simulations as structured, agent-callable interfaces
• Design agentic workflows for network self-service: automated troubleshooting, circuit provisioning assistance, and proactive anomaly remediation
• Evaluate and recommend open source frameworks (LangGraph, LlamaIndex, OpenTelemetry, gRPC, GraphQL) to accelerate network intelligence delivery

• Build production-grade Network APIs in Python or Java that consume observability telemetry, asset inventory, and topology data
• Implement MCP servers that expose network capabilities as callable tools and data resources for LLM-powered agents
• Develop agentic pipelines using frameworks such as LangGraph or similar that enable customers to perform network self-service tasks through natural language and structured tool calls
• Integrate across observability stacks (Prometheus, OpenTelemetry, Kafka, Grafana Loki) and network asset systems to create coherent, queryable data surfaces
• Apply AI-native development practices: prompt-engineered tool definitions, retrieval-augmented generation (RAG) from network knowledge bases, structured outputs for agent decision loops
• Participate in peer code reviews and champion standard coding practices across the team

• Develop unit and integration tests for Network APIs, MCP server endpoints, and agent tool invocations
• Build test harnesses that simulate agent interactions with MCP-exposed network capabilities to validate safety, accuracy, and latency bounds
• Define testing strategies for agentic systems including adversarial prompts, tool misuse scenarios, and multi-step reasoning failures
• Recommend and adopt appropriate testing frameworks (pytest, contract testing, LLM evaluation suites)

• Define deployment and release guidelines for AI-native network services, including canary strategies for MCP server rollouts
• Manage environment planning for observability data pipelines, agent runtimes, and network API backends
• Document software configuration management practices and educate cross-functional teams on agentic deployment patterns and MCP lifecycle management

• Maintain and continuously enrich the inventory of production network APIs, MCP servers, agent workflows, and observability pipelines
• Prepare application support documentation covering agent failure modes, tool call error handling, and network API degradation runbooks
• Troubleshoot production issues across the AI-native stack: data pipeline gaps, agent reasoning failures, and MCP tool contract violations

• Collaborate with Equinix customers and internal NOC teams to co-design self-service network experiences powered by AI agents
• Translate customer network pain points capacity planning friction, manual troubleshooting cycles, slow provisioning into agent-driven automation capabilities
• Represent Network Observability engineering in cross-functional forums on AI platform strategy and network developer experience

• Continuously evaluate emerging AI and networking technologies: new LLM capabilities, MCP protocol evolution, network foundation models, and vector-native data systems
• Prototype and evaluate AI-native approaches to network observability: anomaly detection agents, natural language network queries, and predictive capacity agents
• Contribute to Equinix's internal AI engineering community by sharing learnings from agentic network system design

• 5+ years of software engineering experience with a focus on platform, infrastructure, or network software systems
• Demonstrated experience building APIs that consume observability, telemetry, or network data at scale
• Proficiency in Python and/or Java for backend API and data pipeline development
• Hands-on experience with AI/LLM application development: prompt engineering, tool calling, RAG pipelines, or agent frameworks