GTM AI Engineer

About the Role & Hiring Manager

II joined Zenity because it’s a rare opportunity to help build the operational foundation for a new security category focused on securing AI. The team is operating at a high level and moving quickly to define how security works in a world of AI agents.

As a leader, I value ownership, collaboration, and high standards. Our team moves with urgency and holds a strong sense of accountability to the business. I’m looking for builders who communicate proactively, take ownership of problems, and drive solutions that create measurable impact.

This role sits at the center of our AI-driven revenue engine. You’re not just an engineer, you’re a builder who understands GTM strategy and uses AI to improve how our Sales, Marketing, Field Engineering, and Partner teams operate. Your work will turn early AI prototypes into reliable production systems that automate lead research, personalize outreach at scale, and provide real-time insights across the revenue lifecycle.

About Zenity

Zenity is the leader in AI Agent Security and the first company to bring an agent-centric security platform to market. As enterprises accelerate AI agent adoption, we are establishing the security framework for how AI agents are secured and governed at enterprise scale.

We deliver full-lifecycle visibility, governance, detection, prevention, and response for AI agents from build time to runtime, across SaaS, home-grown platforms, and end-user devices. Backed by $55M+ in funding, including a $38M Series B with strategic investment from Microsoft’s M12, Zenity is trusted by Fortune 500 enterprises globally.

Join us in shaping how AI agents are secured at enterprise scale.

What You’ll Do

• Design AI Systems for GTM: Build AI-driven workflows, applications and agents that automate and enhance GTM activities such as lead prioritization and enrichment, competitive intelligence, sales research, and pipeline insights.
• Build Production AI Applications: Develop and deploy scalable LLM-based solutions integrated with our GTM stack (HubSpot, Slack, Outreach, Gong and internal tools).
• Develop AI Workflows and Integrations: Design prompting strategies, orchestration logic, and tool integrations that enable AI systems to retrieve data, reason over context, and take action across internal systems.
• Build AI Data Infrastructure: Create retrieval pipelines and knowledge systems (e.g., RAG) connecting AI applications to product documentation, sales collateral, and customer insights.
• Evaluate and Optimize AI Systems: Implement evaluation frameworks to measure quality, reduce hallucinations in customer-facing workflows, and optimize performance and cost.
• Prototype and Scale AI Capabilities: Identify high-impact AI opportunities with GTM leadership, prototype quickly, and scale successful solutions, including autonomous agents into production.

• Strong Foundation in Logic & Code: You’re comfortable writing code (ideally Python or Node.js) to automate tasks, build integrations, and solve problems. You don’t need a CS degree, but you have a builder’s mindset and can navigate technical documentation independently.
• Experience Building AI Applications or Workflows: You’ve built AI-powered systems, either professionally or through serious personal projects, that go beyond simple chatbots. This could include intelligent workflows, automation tools, copilots, agents, or autonomous processes powered by LLMs.
• Systems Integration & APIs: You’re comfortable connecting systems through APIs so AI workflows can interact with tools like CRMs, messaging platforms, databases, and internal services.
• Data & Context Management for AI: You understand that AI systems are only as good as the information they receive. You know how to structure context and data sources (e.g., databases, search tools, document libraries, RAG pipelines) to improve output quality.
• Architectural Thinking: You can take a messy human workflow (e.g., how a sales team researches prospects) and translate it into structured logic that an AI system can execute.
• Bias for Action: You move fast and learn by shipping. You’d rather prototype quickly, test in the real world, and iterate than over-engineer something before it’s used.
• Curiosity & Debugging Mindset: You enjoy troubleshooting AI behavior, digging into why outputs fail, hallucinate, or break and iterating on prompts, logic, and system design to improve reliability.