Applied AI Engineer

About the Role & Hiring Manager

I 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.

We're hiring an Applied AI Engineer to build and own production AI systems that power Zenity's GTM motion. This is not a research role or a prototype-and-handoff role. You will design, ship, and maintain LLM-based applications and agentic workflows that run in production, integrated into the tools our sales, marketing, and customer success teams use every day. You'll work directly with GTM leadership to prioritize a high-volume pipeline of AI projects and bring a point of view on what to build next and why. 

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

• Production LLM Applications: Build and deploy LLM-based applications integrated with HubSpot, Slack, Outreach, Gong, and internal tools. Production-quality Python, context and token management, retry logic, and FastAPI services that are reliable and observable.
• Agentic Workflows: Architect multi-step agents using LangChain, LangGraph, or Claude's tool-use APIs. Explicit state management, separated planning and execution, and human-in-the-loop checkpoints where production data is involved.
• RAG Pipelines: Connect AI systems to sales collateral, competitive intel, and customer call data. Own the full stack: chunking, embeddings, vector storage (pgvector, Pinecone, Qdrant), hybrid search, and retrieval evaluation.
• AI Data Infrastructure: Design the data layer that feeds your systems: ingestion from Gong and HubSpot, structured knowledge stores, and context patterns built on AWS for reliability, not just correctness.
• Evaluation and Quality: Track output quality and hallucination rates, build regression coverage for prompt changes, and version system prompts in source control. Own the feedback loop from production back to improvement.
• Systems Integration: Connect AI workflows to internal systems via REST and webhooks. Design contracts resilient to upstream failures and apply least-privilege tool access, especially in agentic contexts where scoped permissions reduce blast radius.

• Production AI experience: You've shipped AI systems in the real world, not just demos. You can describe the architecture, the failure modes, and what changed after week one.
• LLM fundamentals: Fluent in prompt engineering patterns, context window management, token tradeoffs, and tool calling. You version system prompts, evaluate them quantitatively, and treat them like code.
• RAG and retrieval: You know the full stack: embeddings, chunking, vector search, hybrid retrieval, re-ranking. You can diagnose degraded recall and fix it.
• Agentic system design: You can translate a messy human workflow into structured agent logic. You know where agents fail, tool overuse, context drift, excessive agency, and how to design against it.
• Systems integration: Comfortable connecting AI workflows to external systems via REST, webhooks, and event-driven pipelines without needing a backend engineer alongside you.
• Python and engineering hygiene: Strong Python, FastAPI or equivalent, AWS or serverless deployment, Git, CI/CD, logging, error handling, alerting.
• GTM domain fluency: You understand how sales and marketing teams work well enough to know when an AI output is useful versus when it will confuse the person receiving it.
• Technical communication: You can explain architecture to non-engineers, write docs a future engineer can use, and flag tradeoffs to GTM stakeholders without requiring them to understand the implementation.

Our interview process is designed to be transparent, conversational, and focused on real-world experience.

• Recruiter Screen (30 minutes) – Learn more about Zenity, the role, and how we work.
• Hiring Manager Interview (45–60 minutes) – A deeper conversation about your background, experience building AI systems, and how you approach solving problems.
• GTM RevOps Interview (45 minutes) – A discussion with a member of the GTM RevOps team about how AI systems and automation can support revenue teams and real business workflows.
• Technical Interview (45 minutes) – A conversation focused on your technical skills, how you build and ship AI systems, and how you collaborate with technical and business teams.
• Cross-Functional Peer Panel (45 minutes) – Meet with peers across GTM and engineering to discuss how you work across teams and approach real-world challenges.

Please note that the interview process may evolve slightly based on scheduling and team availability.



Zenity is proud to be an equal opportunity employer. We enable enterprises to adopt AI agents securely and at scale, and that starts with building a team that reflects a wide range of perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability, age, veteran status, or any other protected characteristic.

We’re committed to creating an inclusive environment where talented people can do their best work, securely, confidently, and with impact.

The expected base salary range for this role is $150,000 - $210,000, depending on experience, skills, and location. In addition to base compensation, this role may be eligible for equity and performance-based incentives.