Staff Platform Engineer

At Zencoder.ai, we build and orchestrate AI agents that ship real work - code, research, operations, and more. What started as developer tooling is becoming a platform where people and agents collaborate across knowledge work tasks.

We're looking for an experienced engineer to raise the bar on how our organization builds, tests, ships, and operates software - for both human engineers and AI coding agents.

This is AI harness engineering: the discipline of making the fundamentals (CI/CD, testing, IaC, observability, secure delivery) so solid and well-shaped that agents can work productively inside them. Developer experience and platform engineering are a subset of that work. When the harness is good, humans ship faster and agents stop getting stuck on things that shouldn't be hard.

Security is inseparable from this. AI agents are accelerating both the pace and the shape of vulnerabilities, and secure delivery has to be built into the same stream as everything else - not bolted on. 

Product teams own their services, pipelines, and tests. Your job is to raise the ceiling across the org - defining standards, building leverage, and helping teams level up their workflows, tooling, infrastructure, security posture, and necessary changes to get the most out of coding agents.

This is a hands-on lead role - you'll be deep in the code, infra, and pipelines yourself.

• →Shape the platform and infrastructure that everything else runs on - the foundations product teams build on top of;
• →Improve the full developer loop: local development, remote agents, CI/CD, deployment, observability;
• →Build shared tooling, templates, and golden paths that create real leverage - paved roads teams actually want to use;
• →Stay ahead of the shifting security landscape as AI agents reshape both how vulnerabilities emerge and how they're exploited.

• Strong software engineering background - you will be building both tooling and managing core infrastructure in this role;
• Real fluency with AI coding agents - how they work, where they fail, and what makes them effective;
• A product mindset toward internal tooling - treating engineers and agents as users, and measuring whether the platform actually makes them faster;
• Deep experience with CI/CD, automated testing, IaC, Cloud and developer tooling;
• A passion for software security and secure delivery practices (shift-left / SecDevOps etc) in both the classical context and the new AI agent context.