About the job
tl;dr: If you thrive in a small, hyper-talented team and love keeping complex, high-load production systems resilient—help us scale a system handling millions of real-time messages. As our first dedicated DevOps Engineer, you’ll take full ownership of our cloud infrastructure, security, and developer velocity.

About Us
Zenzap is a professional work-chat app built for everyone. Our customers - from neighborhood nail salons to global banks - get a single, structured, and secure place to talk, share, and get work done without the chaos of consumer chat apps. Every day hundreds of new organizations switch to Zenzap, trusting us to keep their conversations flowing and their data safe and taking them to the next level with professional super powers. We’re backed by top-tier investors, obsessed with great product design, and growing quickly. Our backend team deals with real time event processing, dealing with local first databases sync, building a third party application marketplace and bringing AI super-powers to teams which were stuck in Whatsapp

Role Description

As our Backend AI Team Lead, you will lead a small, elite squad while remaining deeply hands-on in the trenches. You will balance technical guidance with writing production-grade code. Your main challenge will be bridging the gap between heavy distributed systems backend engineering and modern, active AI. We are building autonomous, action-oriented AI agents that actually execute tasks. This means solving complex distributed-system problems, untangling non-deterministic model behaviors, and cracking the architectural challenges of Tool/Function Calling at scale. Our offices are located in Sarona, and we work in a hybrid model with 2 days from the office.

What you’ll do

Lead & Execute: Balance hands-on Python development with practical team leadership, guiding a high-performing squad while remaining a core technical contributor.
Build Active AI Agents: Architect and deploy production-grade agentic workflows, complex tool-calling mechanics, and active orchestration frameworks.
Own the Operational AI Side: Crack the tough nuts of AI in production—handling non-deterministic behaviors, optimizing for latency and cost, and building robust evaluation and guardrail workflows.
Scale Distributed Systems: Design and maintain resilient, enterprise-grade architectures capable of high-load real-time event processing and seamless database synchronization.
Collaborate for Scale: Work closely with cloud infrastructure, containers, and event-driven pipelines to ensure our AI features scale smoothly without compromising stability.

What You Bring

5-10+ years of backend experience building large-scale, enterprise-grade distributed systems.
Deep Tech Stack Mastery: Expert-level proficiency in Python, and solid experience with cloud platforms (GCP or AWS), containers, and event-driven architectures (Pub/Sub, Redis).
Production-Grade AI Experience: Proven, hands-on experience integrating LLMs and pushing agentic features into production. Deep understanding of tool calling/function calling and orchestration.
Leadership Track Record: Previous practical experience as a team leader or tech lead.
Strong System Design Skills: Deep understanding of real-time event processing, queuing systems, and data synchronization.
An Engineering Mindset for AI: You understand that AI in production requires rigorous software engineering—focusing on evaluation, guardrails, and deterministic testing for non-deterministic systems.

Bonus Points

Experience building AI agent harnesses or advanced agentic frameworks from scratch.
Strong familiarity with both GCP and AWS environments.