Job Opportunities API

About Netskope Today, there's more data and users outside the enterprise than inside, causing the network perimeter as we know it to dissolve. We realized a new perimeter was needed, one that is built in the cloud and follows and protects data wherever it goes, so we started Netskope to redefine Cloud, Network and Data Security.  Since 2012, we have built the market-leading cloud security company and an award-winning culture powered by hundreds of employees spread across offices in Santa Clara, St. Louis, Bangalore, London, Paris, Melbourne, Taipei, and Tokyo. Our core values are openness, honesty, and transparency, and we purposely developed our open desk layouts and large meeting spaces to support and promote partnerships, collaboration, and teamwork. From catered lunches and office celebrations to employee recognition events and social professional groups such as the Awesome Women of Netskope (AWON), we strive to keep work fun, supportive and interactive.     Visit us at  Netskope Careers. Please follow us on LinkedIn and Twitter @Netskope . Moving AI agents from a playground demo to production requires serious software engineering discipline. We are looking for an AI Agent Engineer who knows how to build scalable, secure, and fully auditable agentic systems. In this role, you won't just tweak prompts until they "feel right." You will build spec-driven orchestrator and sub-agent architectures, manage sensitive data classifications, integrate tool gateways, and establish comprehensive test cases. You’ll work closely with Product, Security, and Cloud Architecture to ensure every agent operates strictly within its designated guardrails and risk tiers. Skills and Competencies: Write full agent specifications — purpose, capabilities, input/output contracts, guardrails, and tool boundaries — to the same level of rigor a safety-critical system would get, not a quick README. Design and build orchestrator agents that hand work off to specialized sub-agents, and hold the line on a simple rule: the orchestrator coordinates, it doesn't do the work itself. Write and test system prompts, and design tool schemas that stay accurate even when an agent has to choose between fifteen or twenty different tools — not just three. Work with the Data Steward to classify how sensitive each agent's data is, and with the Product Owner/Architect to set its risk tier, before anything goes up for registration. For each capability an agent has, decide whether it should be deterministic logic or model judgment, and build test cases that prove it actually meets that bar. Build every agent against the platform's tool gateway, so nothing an agent does falls outside what it's registered and approved to touch. Design for scale from day one — sessions that stay isolated from each other, failures that are predictable, and no assumptions that only hold up in a single test run. Prepare the evidence a reviewer needs before approving an agent for production: test results, guardrail coverage, and behavioral results, laid out clearly enough that someone outside your head can follow the reasoning. Must-Have: At least 4 years building production software, with 1–2 of those years specifically spent building LLM-powered agents — not chatbots, not RAG pipelines, agents that make tool-use decisions on their own. Real hands-on time with at least one agent orchestration framework — LangGraph, Bedrock AgentCore, CrewAI, AutoGen, or similar — enough to explain why you'd pick one over another for a given problem. Working knowledge of the Model Context Protocol: building or integrating MCP servers and clients, and understanding where tool-selection tends to break down. A real prompt engineering practice — versioning changes, testing them against a set of cases before shipping — not just iterating in a playground until it feels right. Comfort writing precise specs: boundaries, f