{"id":2476934,"url":"https://alion.io/job/equinix-network-software-engineer-observability-ai","title":"Network Software Engineer - Observability & AI","company":{"id":7655,"name":"Equinix","domain":"equinix.com","url":"https://alion.io/company/equinix","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"Networking","role_family":"Networking","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":51000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":540},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AutoGen","optional":false},{"name":"BGP","optional":false},{"name":"Configuration Management","optional":false},{"name":"Function Calling","optional":false},{"name":"Grafana","optional":false},{"name":"GraphQL","optional":false},{"name":"gRPC","optional":false},{"name":"Java","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Loki","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"MPLS","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Weaviate","optional":false},{"name":"PostgreSQL","optional":true}],"status":"live","first_seen_at":"2026-10-13T21:40:33Z","employer_posted_date":"2026-10-13","last_verified_at":"2026-10-14T02:00:34Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Who are we?\nEquinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, lifeand planet.\nA place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity toshape their future.\nHelp 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\nWe 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.\nYou 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.\nWhat you will build\nIn 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:\nAI-native Network APIs - high-fidelity, low-latency APIs that expose real-time observability signals and network asset data as structured, agent-queryable surfaces\nMCP 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\nAutonomous 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\nObservability-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\nResponsibilities\nRequirements Analysis\nTranslate complex network and observability requirements into clear API contracts and data models\nPartner with product managers and business analysts to convert network observability use cases - latency anomaly detection, capacity thresholds, topology drift - into actionable engineering requirements\nEvaluate feasibility across the full data stack: streaming telemetry pipelines, asset inventory systems, and AI inference layers\nSoftware Architecture & Design\nParticipate in and drive architectural reviews for AI-native Network API platforms\nDefine reference architectures for exposing network intelligence via MCP servers, ensuring agents can safely query, reason over, and act on live network data\nDesign systems that integrate observability data sources (streaming telemetry, SNMP, flow data, topology graphs) with inventory and asset repositories as a unified programmable layer\nLead the design of AI-native Network APIs that serve both human developers and autonomous AI agents as first-class consumers\nArchitect MCP tool and resource definitions that expose network capabilities - topology queries, health checks, capacity lookups, change simulations - as structured, agent-callable interfaces\nDesign agentic workflows for network self-service: automated troubleshooting, circuit provisioning assistance, and proactive anomaly remediation\nEvaluate and recommend open source frameworks (LangGraph, LlamaIndex, OpenTelemetry, gRPC, GraphQL) to accelerate network intelligence delivery\nDevelopment & Coding\nBuild production-grade Network APIs in Python or Java that consume observability telemetry, asset inventory, and topology data\nImplement MCP servers that expose network capabilities as callable tools and data resources for LLM-powered agents\nDevelop 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\nIntegrate across observability stacks (Prometheus, OpenTelemetry, Kafka, Grafana Loki) and network asset systems to create coherent, queryable data surfaces\nApply AI-native development practices: prompt-engineered tool definitions, retrieval-augmented generation (RAG) from network knowledge bases, structured outputs for agent decision loops\nParticipate in peer code reviews and champion standard coding practices across the team\nTesting\nDevelop unit and integration tests for Network APIs, MCP server endpoints, and agent tool invocations\nBuild test harnesses that simulate agent interactions with MCP-exposed network capabilities to validate safety, accuracy, and latency bounds\nDefine testing strategies for agentic systems - including adversarial prompts, tool misuse scenarios, and multi-step reasoning failures\nRecommend and adopt appropriate testing frameworks (pytest, contract testing, LLM evaluation suites)\nDevOps\nDefine deployment and release guidelines for AI-native network services, including canary strategies for MCP server rollouts\nManage environment planning for observability data pipelines, agent runtimes, and network API backends\nDocument software configuration management practices and educate cross-functional teams on agentic deployment patterns and MCP lifecycle management\nSoftware Reliability & Support Engineering\nMaintain and continuously enrich the inventory of production network APIs, MCP servers, agent workflows, and observability pipelines\nPrepare application support documentation covering agent failure modes, tool call error handling, and network API degradation runbooks\nTroubleshoot production issues across the AI-native stack: data pipeline gaps, agent reasoning failures, and MCP tool contract violations\nCustomer & Stakeholder Engagement\nCollaborate with Equinix customers and internal NOC teams to co-design self-service network experiences powered by AI agents\nTranslate customer network pain points - capacity planning friction, manual troubleshooting cycles, slow provisioning - into agent-driven automation capabilities\nRepresent Network Observability engineering in cross-functional forums on AI platform strategy and network developer experience\nR&D & Innovation\nContinuously evaluate emerging AI and networking technologies: new LLM capabilities, MCP protocol evolution, network foundation models, and vector-native data systems\nPrototype and evaluate AI-native approaches to network observability: anomaly detection agents, natural language network queries, and predictive capacity agents\nContribute to Equinix's internal AI engineering community by sharing learnings from agentic network system design\nQualifications\nSkills Required\n5+ years of software engineering experience with a focus on platform, infrastructure, or network software systems\nDemonstrated experience building APIs that consume observability, telemetry, or network data at scale\nProficiency in Python and/or Java for backend API and data pipeline development\nHands-on experience with AI/LLM application development: prompt engineering, tool calling, RAG pipelines, or agent frameworks (LangChain, LangGraph, AutoGen, or equivalent)\nFamiliarity with Model Context Protocol (MCP) - either as a server implementer or consumer - or demonstrated ability to rapidly adopt structured AI tool interface specifications\nExperience with observability stacks: OpenTelemetry, Prometheus, Grafana, Kafka, or similar streaming/metrics platforms\nBachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience\nPreferred\nNetworking Certification or demonstratable understanding of network concepts: BGP, MPLS, VLANs, IP addressing and similar\nFamiliarity with network inventory systems, CMDB/DCIM platforms, or graph-based topology data stores\nBackground in agentic system design: multi-agent orchestration, tool safety guardrails, and structured agent output validation\nExperience with vector databases (Pinecone, pgvector, Weaviate) for RAG-backed network knowledge retrieval\nEquinix 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.\nEquinix 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.\nWe use artificial intelligence in our hiring process. 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