• Build AI agents, workflows, tools and automations that solve real customer, product and operations problems. • Apply LLMs, retrieval, tool calling, evaluations, guardrails and automation to practical financial workflows. • Create AI-assisted experiences for onboarding, support, KYC, risk review, CRM, operations, document handling and product intelligence. • Work with product and engineering teams to identify what is worth building, then build it quickly and improve it in production. • Design around real constraints: accuracy, hallucination risk, latency, cost, security, compliance and user trust. • Turn manual processes into reliable AI-enabled systems that can scale.

• You will own the reliability and quality bar for an AI copilot embedded in an trading platform used by sophisticated investors • You will design and build evaluation systems that measure correctness, safety, latency, and regression risk across market analysis, portfolio/risk reasoning, and trading workflows (including order placement) • You will develop and maintain benchmarks: curated “golden sets,” scenario suites, stress/adversarial cases, and continuously refreshed market/regime-based test corpora • You will build automated quality gates and regression workflows that block releases when key metrics degrade • You will partner with engineering and product to define safe tool/action contracts (deterministic previews, confirmations, auditability) and ensure predictable assistant behavior • You will own model improvement loops tied to evals: data collection/labeling strategies, error taxonomy, prompt/tooling changes, and when appropriate, fine-tuning or preference optimization to measurably improve benchmark performance • You will design and operate monitoring + incident response for AI: telemetry, alerting, RCA, and “fix-forward” processes • You will develop a deep understanding of trading concepts (margin, shorting, portfolio margin, risk, execution) and how to express them accurately and understandably to users

• Build new AI security features into our access proxy product, the point where agent and user traffic is inspected and controlled. • Treat protocols such as Model Context Protocol (MCP) as enforcement boundaries, and build controls for agent-to-agent communication. • Connect that product to the Delinea Platform and prove the experience with design and paying customers on live capability. • Move those features into the Delinea Platform as the product matures, keeping customers running smoothly throughout the transition. • Enforce just-in-time access and policy decisions in the request path, drawing on the platform's identity and authorization building blocks. • Partner with product and with the teams building the platform's proxy and access engine.

• Build AI agent discovery on top of Delinea's existing cloud discovery rather than standing up a separate inventory, covering Microsoft Azure today and expanding to AWS and Google Cloud as the platform adds those clouds. • Build discovery for AI agents running on endpoints, a newer surface for the team. • Assess the security posture of discovered agents: how they are configured, what they can access, and where they create risk. • Feed discovered agents and their context into our identity and attribution layer so they can be governed. • Partner closely with the platform teams whose discovery services your work builds on.