Network Software Engineer - Observability & AI at Equinix in Bangalore Office BLS2

Job description

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, g