AI Native Builder/Architect

WHO WE ARE

The company 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 the company solves.

Gartner has named context the defining issue in enterprise AI, and calls context graphs the essential infrastructure for AI agents, naming the company 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.

WHO WE ARE

Gartner calls context the defining issue in enterprise AI, and context graphs the essential infrastructure for AI agents. It names the company as one of three vendors already building one. We're also the only vendor named a Leader in all four major Gartner and Forrester evaluations for data catalogs, data governance and metadata management, the layer our context graph is built on.

Come build the infrastructure AI runs on.

THE TEAM

You'll join the team building the company's context layer, the platforms that make it possible and the vertical agents that run on it. The platform is what any agent needs before an enterprise will trust it: a registry of which agents and skills exist, traces and evals on every run, and governance over cost and access.

Vertical agents each do one specific job, for customers and for the company's own teams. Inside the company they already run real work. There's an agent our CSMs trust, three GTM agents that replace 3,000 calls made by hand, an agent that watches every page on the company [link for members] and one that monitors the other agents. We write up what we learn in the open at Becoming Frontier [link for members]

The team works in small "two-slice" units. You own one initiative, usually with one or two people alongside you, and you decide how it gets built.

WHAT WILL YOU DO?

You'll take problems nobody has fully defined yet and ship working systems, fast. Depending on where you're matched, you may:

• Own a 0→1 initiative from problem to production. Frame it, write the spec when there isn't one, pick the architecture and build it.

• Build agents with the people who own the outcome, inside the company or at a customer. Scope the workflow with them, demo early and put the agent where the work already happens, like an approval in Slack or a redline in a legal team's Word doc.

• Earn autonomy with evidence. Start with shadow runs, study the misses and widen what the agent can do as results support it.

• Design for failure. Decide what happens when a tool is down, context is thin or the agent isn't sure, and how a person takes over.

• Make the engineering calls on what stays deterministic code and what needs a model, build vs. reuse, model choice, cost and reliability. Explain the trade-offs clearly.

• Make agent behavior observable. Tie traces and evals to the revision that ran. Track consistency, cost and latency alongside success rate, and treat drift as a regression.

• Build on and extend the context layer, agent platform and registry: versioned skills, layered instructions, explicit permissions for tools and secrets, and model changes handled as migrations with evals rerun before promotion.

• Build AI-native yourself. Run coding agents on your own work, keep their skills and instructions up to date, and review what they write for architectural drift.

• Share what you learn. Turn what works into skills, reference architectures and quality gates that others adopt, and demo what you've built.

WHAT MAKES YOU A GREAT MATCH FOR US?

MUST HAVE

• You've built software systems hands-on and shipped 0→1 products or platforms into production, often without a spec.

• You've shipped agents or LLM systems that real people use. When one went wrong, you traced it back to the context, tool call or decision that caused it.

• You use evals to find and fix real problems, check what a score actually counts, and judge agents by user impact: hours saved, error rate or cycle time.

• You design for failure first: what the agent may touch, what happens when it's wrong, and where a person approves before it acts.

• You build with coding agents every day and hold what they write to the same bar as your own code.

• You're a critical optimist. You believe what agents can do is real, and test what still fails before trusting them with more.

• You reason from first principles. When a familiar design doesn't fit, or a better model makes old scaffolding unnecessary, you notice and rebuild.

• You have strong systems judgment. You can design for production reliability, latency, cost and security, build it yourself, and leave it in a shape others can build on.

• You do your best work owning an initiative with one or two people alongside you, and don't need a large team to feel effective.

STRONG BONUS

• Experience with MCP, A2A, agent frameworks and runtimes, LLM gateways or agent observability, or you've built an agent harness or orchestration layer yourself.

• Experience with knowledge graphs, vector search, retrieval or semantic layers.

• You've built an eval suite from scratch, or run shadow evaluations, LLM grading or staged rollouts.

• You've shipped agents that write to a system of record, with permissions, approvals and an audit trail.

• You've led AI adoption inside an engineering or business team and can show the before and after.

• Founding-engineer, technical-founder or early-stage startup experience, where you built something from nothing.

• A public trail of work, repos, demos, write-ups or talks, that shows how you build.

This role probably isn't for you if you want to manage a large team, want a well-scoped backlog handed to you, would rather build demos than run agents in production, or are still planning to get into AI.

WHY the company?

Joining the company means being part of a global movement to help data teams do their life’s best work. Here’s what you can expect:

• Competitive Compensation: We benchmark at the top of the market and keep compensation simple: strong base salary, performance‑based variable pay, and impact‑driven equity (for most roles), so your total rewards grow in step with the value you create over time.

• AI Native Culture: the company is where AI-native builders come to build the systems the future of work will run on. AI isn’t an add-on, it’s woven into how we build, think, and work every day, empowering every Atlanian to move faster and create a bigger impact.

• Health & Wellness: From Day‑1 health, dental, vision, and mental health to flexible health stipends, we design benefits offerings that lead in each country we're in.

• Flexible Time Off & Leave Policies: We trust you to own your energy: flexible time off and modern leave so you can unplug properly, support yourself and your loved ones, and come back ready to drive an impact.

• Accelerated Growth & Learning: Develop at an uncommon velocity through cutting-edge tech, complex implementations, and an experienced team that values mastery.

• Global, Remote-First, High-Trust: Work from anywhere with a diverse team across 15+ countries, in a trust-first, async environment that gives you true flexibility and ownership over how you work.

More About Us

The company is building the shared context layer that enterprises need so AI can operate on trusted, governed context. The conversation has moved from data leaders asking: “Can we trust the data in our stack?” to businesses asking: “Can we trust AI inside the business?”

We are the missing infrastructure for businesses becoming AI-forward - the connective tissue between their data stack, operational systems, and AI agents.

To learn more, visit [link for members] [link for members] and follow us on LinkedIn. [link for members]

Equal Opportunity Employer

The company is committed to building an inclusive, diverse, and authentic workplace. We do not discriminate based on race, color, religion, national origin, age, disability, sex, gender identity or expression, sexual orientation, marital status, military or veteran status, or any other legally protected characteristic.

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