Nexthink is hiring an AI Agent Engineer

At Nexthink, we are embedding AI agents into the core workflows of our Product organization. We are seeking an AI Agent Engineer to design, build, and continuously improve intelligent agents that augment and automate high-impact product operations activities. This role supports Nexthink’s Product organization, serving a group of 30+ people across Product Management, Product Education, Library, and Product Operations. You will operate in a highly autonomous role, owning the architecture, behavior, and quality of AI agents that improve decision-making, reduce manual work, and increase execution speed. You will combine applied AI engineering with strong systems thinking. The focus is not model research, but production-grade agent design, evaluation, governance, and workflow integration. This role requires strong ownership, product empathy, and comfort operating independently in a production environment with real business impact. What you’ll do Design and architect AI agents - Design reusable AI agent patterns for Product workflows such as research synthesis, PRD (Product Requirement Document) drafting support, roadmap analysis, operational triage, and knowledge retrieval. - Define agent instructions, tool schemas, guardrails, and structured output contracts. - Architect human-in-the-loop decision boundaries where appropriate. Integrate agents into product workflows - Integrate AI agents into Microsoft 365, Copilot Studio, Power Platform, AWS services, and internal systems. - Define and maintain action interfaces between agents and deterministic systems (APIs, workflows, runbooks). - Ensure reliable orchestration across conversational and workflow layers. Own quality, evaluation, and governance - Define measurable success criteria for agent outputs. - Build lightweight evaluation frameworks and regression testing for prompts and behavior. - Monitor output quality, hallucination risk, cost, and drift over time. - Establish safe usage guidelines and governance boundaries. Operate and improve in production - Monitor agent usage, reliability, and impact. - Optimize performance, token efficiency, and response structure. - Iterate on agent behavior based on real usage patterns and feedback. - Proactively identify new opportunities where AI agents can improve Product workflows.