Senior AI Engineer

Zafran is building the next generation of vulnerability and risk management. We bring together complex security data and turn it into clear investigation, prioritization, and action. We're creating a new AI team to build the shared infrastructure, tools, and standards that enable teams across R&D to develop reliable AI-powered capabilities. The team will also own and deliver core AI experiences used across Zafran's platform and organization.

About Zafran: Our Mission: To stop the exploitation of vulnerabilities, everywhere. What makes us different: Zafran de-risks 90% of critical vulnerabilities overnight across your hybrid environment, leveraging Agentic Capabilities and your existing security tools to rapidly mitigate and remediate the 10% most likely to be exploited. Who’s behind us: Zafran is backed by Menlo Ventures, Sequoia Capital, Cyberstarts, and a deep belief that cybersecurity should move as fast as attackers do.

We’re one of the fastest-growing companies in the industry, scaling to meet demand from the world’s most advanced, security-obsessed organizations. We’re serious about our mission- so expect work that matters, teammates who challenge and inspire you, and plenty of fun along the way! We're looking for a Senior AI Engineer to be one of the team's first engineers and to own Zafran's AI infrastructure end to end — the shared platform every R&D team uses to build, test, ship, and monitor AI features.

That platform powers our in-product AI assistant, our background agents, and every AI capability still to come. You'll set the standard for how AI runs in production at Zafran: when a team ships an AI feature, it should be reliable, secure, observable, and cost-efficient by default — because your platform made it that way. What you will do: - Own the AI platform. Design, build, and run the shared agent runtime: model access, tool use, memory and sessions, streaming, and human approval for sensitive actions. - Production-grade.

Scaling, fallback between model providers, rate limits, timeouts, and safe rollouts on Kubernetes. - Own evaluation. Build the evaluation framework every team uses to measure quality, correctness, latency, and cost — and catch regressions in CI before customers do. - Own observability and cost. Tracing for every agent run and tool call, token spend per tenant and per feature, and alerts when quality or cost drifts. - Own AI security.

Strict tenant isolation, defenses against prompt injection, and guarded data access across a multi-tenant security platform. - Build the tool layer. MCP tools and data connectors that let agents query our security data safely and reliably. - Enable R&D. Shared libraries, templates, and docs so other teams can build, test, deploy, and monitor AI features independently. - Stay hands-on with architecture, implementation, and production issues, and help raise the AI engineering bar across R&D.

Requirements: - 5+ years of backend or infrastructure engineering, with strong system design skills. - Proven experience building and operating production-grade AI / LLM infrastructure — real users, real traffic, real incidents. Not prototypes. - Deep hands-on experience with agentic systems: tool use, structured outputs, prompt and context management, retrieval, and long-running or streaming flows. - Experience designing and running LLM evaluations at scale. - Strong Python skills. - Production experience on AWS and Kubernetes, including observability (tracing, metrics, logs). - Ownership mindset — you treat the platform as a product and other engineers as your customers.

Experience with the following is a plus: - Experience with Amazon Bedrock, Strands Agents, LiteLLM, MCP, or OpenTelemetry (our current stack). - Experience building internal developer infrastructure used by multiple engineering teams. - Experience with model evaluation, safety controls, governance, or LLM security (guardrails, prompt injection). - Experience operating multi-tenant SaaS systems. - Background in cybersecurity or security-data products. - Familiarity with Go, ElasticSearch, ClickHouse, or Postgres.

Why this role matters: AI is a shared capability across Zafran's products and engineering organization. As the owner of the AI infrastructure, you'll build the foundation that lets every R&D team ship AI features faster and more safely — and shape Zafran's AI architecture from the ground up.