AI Agent Developer

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About the role

Join the Future of Security at Netskope Netskope (NASDAQ: NTSK) is a leader in modern security and networking for the cloud and AI era. We secure and accelerate cloud, data, and AI in real time, everywhere. Thousands of customers, including more than 30 of the Fortune 100, trust the Netskope One platform, its Zero Trust Engine, and the powerful NewEdge network to gain full visibility and control without performance trade-offs. At Netskope, our technology is driven by our greatest strength: our people. We believe that belonging powers innovation, and success is both personal and organizational. We embrace differences in gender, ethnicity, beliefs, ability, and identity, creating an environment where every voice is heard and respected. We empower our employees to bring their authentic selves to work, grow their careers through continuous education and mentorship, and lead with transparency and curiosity. Join a team where you belong, where you are encouraged to be an entrepreneur, and where together, we continue to redefine the landscape of security. Visit Careers at Netskope to learn more. Follow us on LinkedIn and Instagram . 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

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