Senior Product Engineer - Backend

About Evoke

AI Agents are rapidly becoming the new operating system for enterprises, yet they are creating a massive, hidden security debt that is compounding daily. At Evoke, we aren’t just watching this shift happen. We are securing it. Our mission is to surface and control the risks that agents pose to businesses. Just as EDR did for endpoints, we are doing for agents with the industry’s first true Agent Detection and Response platform.

About The Role

As a Senior Product Engineer - Backend, you will build the backend services and agent integrations that power the Evoke platform: the APIs, data models, and pipelines that ingest and process agent telemetry. That work spans both the inventory of what is installed across an organization and the runtime telemetry of what agents are actually doing, the streams of activity that feed threat modeling, detection, and response.

• Design, develop, and deliver backend features, data models, integration contracts, and APIs that fit the existing architecture
• Build ingestion pipelines that keep up under load without losing or double-counting events
• Handle two very different shapes of telemetry: periodic inventory reports that arrive stale or incomplete, and continuous runtime activity that arrives at high volume, late, or duplicated across capture paths
• Design for tenants that vary in size by orders of magnitude
• Agree on contracts with frontend and other engineers early, and ask the questions that turn a loose requirement into something buildable
• Write typed, tested, observable code, and verify that what you shipped actually solved the stated problem
• Support the platform as critical issues arise: debug across process, service, and data boundaries, then reproduce, isolate, fix, and confirm

• 5+ years of backend engineering experience, with expert-level Python, including async programming
• Deep experience with SQL and NoSQL databases, including queries, indexing, and data modeling
• Track record of writing typed, tested application code, including data modeling with a validation library such as Pydantic
• Working knowledge of REST and GraphQL API implementation, telemetry ingestion and batch pipelines, and durable workflow orchestration
• Comfort with containerization, deployment pipelines, cloud infrastructure, and application observability

• Rust or data-lake experience
• TypeScript and modern frontend frameworks, enough to work well with the engineers consuming your APIs
• Experience with ML/AI systems, language models, security tooling, or threat detection

• Pragmatic Problem Solving: A focus on practical, deployable solutions that move the product forward. You prioritize resolving customer and platform issues efficiently, while balancing immediate needs with long-term maintainability and support.
• Curious & Resilient Mindset: Operates effectively in environments with evolving requirements and incomplete information. Adapts to change, learns new systems and domains quickly, and maintains attention to detail while iterating toward solutions.
• Effective Communication: Communicates technical concepts, progress, and blockers clearly to engineers and product partners. Writes well-structured documentation. Helps ensure shared understanding and alignment without unnecessary complexity.
• Collaborative Spirit: A commitment to teamwork, empathy, fostering an environment of continuous learning, and shared success. Supports the growth of other engineers by providing thoughtful feedback and helping reinforce good engineering practices.
• Results-Oriented: Focuses on delivering high-quality outcomes. Takes responsibility for completing work reliably and follows through on issues and improvements within their scope of ownership.