Staff Software Engineer - Infrastructure (AI Native)

Who We Are

Atlan is building the context layer for enterprise AI. Enterprises are pouring money into AI and most of it dies in production because the AI does not understand the business context around the data. That is the problem Atlan solves.

Gartner has named context the defining issue in enterprise AI, and calls context graphs the essential infrastructure for AI agents, naming Atlan one of three vendors already building it. We are also the only vendor named a Leader across all four major Gartner and Forrester evaluations for data catalogs, data governance and metadata management, the foundation the context layer runs on.

Come build the infrastructure that AI runs on.

The Opportunity

AI applications are moving from prototypes to production, and the infrastructure underneath them is becoming increasingly complex.

We build and run the infrastructure behind our AI products: the cloud and Kubernetes platform they run on, the pipelines that ship code to production, and the observability that tells us when something breaks. We’re also building AI agents that can operate that infrastructure themselves

We’re looking for a Staff Software Engineer who can work across these areas, build systems from the ground up, and think ahead about how they will behave at scale.

A key part of this role is building AI agents for infrastructure and engineering workflows. We’re looking for someone who has actually built and shipped agents or agentic systems that perform real work, not just used AI coding assistants or experimented with prompts.

What You Will Do?

• Build reliable infrastructure at scale. Design and improve cloud infrastructure, Kubernetes, networking, compute, storage, and distributed systems.
• Design for failure. Identify bottlenecks, failure modes, and operational risks before they become production issues.
• Build safer deployments. Improve CI/CD, testing, rollout strategies, and deployment controls.
• Build strong observability. Develop monitoring, alerting, tracing, and operational tooling that makes systems easier to understand and operate.
• Improve infrastructure efficiency. Identify opportunities to improve performance, reliability, and cloud cost.
• Build AI agents for infrastructure and engineering. Design and ship agents that can use tools and APIs, execute multi-step workflows, troubleshoot issues, or automate operational work.
• Make agents safe to operate. Build the right evaluation, permissions, guardrails, observability, and failure handling around agents taking real actions.
• Raise the engineering bar. Mentor engineers through architecture reviews, code reviews, technical discussions, and postmortems.
• Share on-call ownership. Join the team’s on-call rotation, respond to production incidents, and turn what you learn into fixes, runbooks, and automation that reduce future pages.

What Makes You a Match?

• You’ve built AI agents, not just used AI tools. You have shipped agentic systems that perform meaningful work and understand tools, state, workflows, evaluation, and guardrails.
• You have strong infrastructure fundamentals. You understand distributed systems, cloud infrastructure, Kubernetes, networking, and production operations.
• You’ve built systems through scale. You have taken infrastructure from relatively early stages to meaningful production scale and can talk about the trade-offs along the way.
• You think about reliability by default. Failure modes, blast radius, operational complexity, and recovery are part of how you approach architecture.
• You can debug across layers. You’re comfortable moving from application behaviour to Kubernetes, networking, and cloud infrastructure to find root causes.
• You build rather than operate manually. Your instinct is to automate repetitive work and build tools that make engineering teams more effective.
• You’ve built platforms other engineers build on. Package registries, artifact stores, CI systems or internal developer platforms all count.
• You write things down. At staff level most of your influence comes through design docs and reviews.

Strong signals: Experience with multi-tenant SaaS, distributed systems, infrastructure automation, or production-grade AI agents. Experience with agent orchestration, tool use, evaluation, memory/state, or agent reliability is particularly valuable.

Our stack: AWS/EKS, Kubernetes, OpenTof…