AI Engineer

About the job

Overview: StoneX is strengthening its cybersecurity defenses in a rapidly evolving 2026 threat landscape where adversaries are increasingly using AI to evade traditional detection. Our Information Security team is building next-generation detection capabilities that go beyond static rules and signatures—leveraging autonomous AI agents and targeted use cases to detect, investigate, and respond to sophisticated threats in real time. We are expanding our Detection Engineering team with a dedicated focus on AI agent development and security use case engineering. This specialized group designs, prototypes, and productionizes intelligent AI agents that automate complex detection workflows, enrich alerts with contextual intelligence, and continuously adapt to emerging attack patterns. We are seeking a motivated AI Engineer to join the Detection Engineering team with a primary emphasis on AI agent development and use case development. In this hands-on, entry-level role you will work directly with senior detection engineers to identify high-impact security use cases and build production-ready AI agents that enhance threat detection accuracy, accelerate alert triage and investigation, reduce false positives, and enable proactive defense. Collaborating closely with senior detection engineers, threat hunters, security analysts, incident responders, and SOC teams, you will gain immediate exposure to cutting-edge agentic AI applied to real-world cybersecurity challenges. This is an outstanding prospect for an early-career professional to develop expertise in LLM-powered agents, agent orchestration frameworks, and security-specific use cases while making tangible contributions to protecting the organization. Responsibilities: - Collaborate with detection engineers and security stakeholders to identify, prioritize, and document high-value AI agent use cases for threat detection, alert enrichment, automated investigation, and response... - Design, develop, and iterate on production-grade AI agents using LLM frameworks to handle multi-step reasoning, tool integration, and decision-making in security workflows... - Support end-to-end agent development lifecycle: prompt engineering, tool creation (e.g., querying SIEM, enriching with threat intel), memory management... - Participate in agent evaluation, monitoring (drift, performance, cost), versioning, and continuous improvement to ensure agents remain effective against evolving threats.