Network Software Engineer - Observability & AI

Who are we?

Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.

A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.

Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Job Summary

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), API’s, 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.

What you will build

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

Responsibilities

Requirements Analysis

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

Software Architecture & Design

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

Development & Coding

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

Testing

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)

DevOps

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

Software Reliability & Support Engineering

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

Customer & Stakeholder Engagement

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

R&D & Innovation

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

Qualifications

Skills Required

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 (LangChain, LangGraph, AutoGen, or equivalent)

Familiarity with Model Context Protocol (MCP) — either as a server implementer or consumer — or demonstrated ability to rapidly adopt structured AI tool interface specifications

Experience with observability stacks: OpenTelemetry, Prometheus, Grafana, Kafka, or similar streaming/metrics platforms

Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience

Preferred

Networking Certification or demonstratable understanding of network concepts: BGP, MPLS, VLANs, IP addressing and similar

Familiarity with network inventory systems, CMDB/DCIM platforms, or graph-based topology data stores

Background in agentic system design: multi-agent orchestration, tool safety guardrails, and structured agent output validation

Experience with vector databases (Pinecone, pgvector, Weaviate) for RAG-backed network knowledge retrieval

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability.  If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer.  All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.

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This posting is a new position within our organization.