<div class="content-intro"><h2><strong>About AlphaSense: </strong></h2> <p style="text-align: justify;"><span style="font-size: 12pt;">AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster, more confident decisions in business and finance. The platform combines domain-specific AI with a vast content universe of over 500 million premium business documents — including equity research, earnings calls, expert interviews, filings, news, and internal proprietary content. Purpose-built for speed, accuracy, and enterprise-grade security, AlphaSense helps teams extract critical insights, uncover market-moving trends, and automate complex workflows with high-quality outputs. With AI solutions like Generative Search, Generative Grid, and Deep Research, AlphaSense delivers the clarity and depth professionals need to navigate complexity and obtain accurate, real-time information quickly. For more information, visit <span><a href="http://www.alpha-sense.com" target="_blank">www.alpha-sense.com</a></span>. Come join us.</span></p></div><h2>About the Role</h2> <p>AlphaSense's GRC function is making a deliberate investment in automation and engineering, and we need a Senior GRC Engineer to lead that charge. You will design and build the technical automation that powers our compliance testing, evidence collection, risk monitoring, and GRC platform integrations—and you will own the governance architecture for how AI and agentic systems are built and deployed at AlphaSense. You sit at the intersection of security engineering and compliance operations. You are not just AI-curious; you are AI-native in practice: you use LLMs and agents for real substantive work—analysis, drafting, automation, code, investigation—and you apply rigorous judgment about where AI creates leverage, where it introduces risk, and where a human must remain in the loop. The goal is continuous compliance infrastructure that generates its own proof, not a compliance team that sprints to collect evidence before each audit cycle.The conventional GRC playbook was not built for a company like AlphaSense—and we are not following it. We are building a GRC engineering function that we believe represents what the entire industry will eventually need to become: automated, AI-native, and architected like a product rather than operated like a process. The frameworks are still catching up. The tooling is still maturing. We are doing the work now that others will reference later. If you want to build something that sets the standard—not follow one—this is the role.</p> <h2><strong>Key Responsibilities</strong></h2> <table style="border-collapse: collapse; border-color: black; border-style: solid;" border="1"> <tbody> <tr> <td style="border-color: black;"> <h4>Compliance Automation Engineering</h4> </td> <td style="border-color: black;"> <p>Design, build, and maintain automated compliance testing pipelines that continuously validate control adherence across cloud infrastructure, SaaS platforms, and internal systems. Replace point-in-time manual evidence collection with always-on automated checks. Pull evidence directly from APIs—cloud audit logs, identity systems, configuration posture, secrets management—without relying on screenshots or control owner self-attestation.</p> </td> </tr> <tr> <td style="border-color: black;"> <h4>GRC Platform Engineering & Integration</h4> </td> <td style="border-color: black;"> <p>Own the technical configuration and integration of the GRC platform (Drata or equivalent). Build custom API integrations, automated evidence connectors, and workflow automation that reduce manual control owner burden and keep evidence artifacts current. Know where the platform solves the problem and where custom code is the better answer.</p> </td> </tr> <tr> <td style="border-color: black;"> <h4>AI-Native GRC Workflows</h4> </td> <td style="border-color: black;"> <p>Develop and deploy AI-assisted workflows for evidence summarization, control narrative drafting, risk analysis, vendor questionnaire triage, and policy gap detection. Build with LLMs and agentic frameworks as a professional standard—not an experiment. Apply domain-specific judgment: know where AI helps, where it hurts, and where sensitive data, attacker-controlled inputs, or privileged access require a human in the loop.</p> </td> </tr> <tr> <td style="border-color: black;"> <h4>AI & Agentic Systems Governance</h4> </td> <td style="border-color: black;"> <p>Own the governance framework for AlphaSense's AI and agentic systems. Define the policies, control sets, and compliance posture that govern how agents are built and deployed—and build ahead of the compliance frameworks that are still catching up. Anticipate new policy requirements, adapt existing controls, and ensure the governance architecture is ready before auditors ask about it.</p> </td> </tr> <tr> <td style="border-color: black;"> <h4>Policy as Code & Control Architecture</h4> </td> <td style="border-color: black;"> <p>Build the policy program as code: policies in version control, peer-reviewed, with requirements expressed as enforceable rules and automated checks rather than static documents. Design a common controls framework that satisfies SOC 2, ISO 27001, ISO 42001, and future frameworks from a single control reference—no rework across frameworks.</p> </td> </tr> <tr> <td style="border-color: black;"> <h4>Continuous Control Monitoring & Observabil