Software Engineer, Agentic AI Infrastructure

In this role, you will

• Design and build the distributed, fault-tolerant infrastructure that powers Anrok's AI agent capabilities—covering memory, state management, and execution for autonomous systems.
• Own technical architecture decisions for new agent infrastructure services end-to-end, from initial design through production deployment and iteration.
• Build the evaluation, observability, and reliability tooling that lets the rest of the team ship agents confidently on top of your infrastructure.
• Collaborate closely with product engineers and our tax data team to translate real compliance workflows into reliable, scalable agent primitives.
• Drive engineering best practices across the team: code quality, testing discipline, system design, and operational health.
• Identify and fix performance, reliability, and scalability bottlenecks as our agent systems grow.
• Help grow the team—mentoring engineers, contributing to technical culture, and raising the bar on how we build.

What excites us

• You have a strong systems engineering background, with 3+ years of experience building and operating distributed, production-grade systems.
• You've designed services that are scalable, resilient, and fault-tolerant—and you've done it more than once.
• You've built and deployed AI agents or LLM-powered systems in production, and you understand firsthand what makes them reliable and maintainable at scale.
• You're familiar with the modern AI stack—prompt engineering, eval frameworks, RAG pipelines, tool calling, and agent orchestration—and you have a point of view on what the infrastructure beneath it should look like.
• You think carefully about architecture and tradeoffs, and you're comfortable leading technical decisions without a complete playbook.
• You have experience with the full software development lifecycle: design, code review, testing, deployment, and on-call ownership.
• You're energized by ambiguity—you can take a fuzzy problem, break it down, and ship something real.
• You're curious and motivated to learn new tools—you've experimented with AI in your work or on your own, you're excited about what's possible, and you've built something with it (an application, a workflow, a creative solution to a real problem) that you can walk us through.