Principal Applied AI Architect

Order.co is the System of Action for the Office of the CFO, transforming the way businesses purchase and pay into an intuitive, B2C-like shopping experience. Order.co leverages embedded AI agents and embedded financial products to reinvent the way businesses connect with their vendors. End users enjoy a seamless, zero-training buying experience, while finance and procurement leaders gain a single platform to orchestrate how the business “should operate”. The result is an all-in-one solution that serves as a gravitational pull for spend and data, automating and eliminating procurement and finance workflows from requisition to reconciliation along the way. Order.co is on the cutting edge of B2B Agentic Commerce, poised to be the market leader in creating a more predictive, prescriptive, and personalized experience for users. Founded in 2016 and headquartered in New York City, Order.co oversees nearly half a billion in annualized spend across hundreds of customers like WeWork, SoulCycle, Lume, and [solidcore]. Order.co has raised $75M in funding from industry-leading investors like MIT, Stage 2 Capital, Rally Ventures, 645 Ventures, and more. Order.co has been proudly named a 50 to Watch by Spend Matters and a Best Place to Work by BuiltIn and Inc. Magazine. Role summary We are hiring a Principal Applied AI Architect to set how data and intelligence work at Order.co: the target-state architecture, the standards that keep it coherent, and the company-level bets that turn proprietary procurement data into durable advantage. This is a senior individual contributor role. You will identify company-level objectives, make the business case to fund them, and land production systems that move primary company KPIs. You will be the Head of Data's closest technical partner: the complementary technical leader to a strategy, product, and business lens. You keep those bets grounded in what we can actually do with the systems we have, and you are the person who knows what it would take to change that. You sit on the data team. Applied AI Scientists take the architecture into product initiatives and own the science through to the metric. Senior AI / Data Engineers take the same architecture into the platform and own the production path. You are the person who holds those two paths on one spine, and who can still get on the keyboard when the first systems need to exist. The work is architecture-first, with a real engineering bar. You will be fluent across stacks, treat security, reliability, and maintainability as delivery criteria rather than afterthoughts, and set a production quality standard that other teams can reuse. You will also stay a working applied scientist: statistical depth, model and agent judgment, and the ability to go from a strategic imperative to a shipped system. This is not an academic role. Understanding production systems and the business they serve is required, and you will get a change through operations and the other stakeholders who gate a real deploy. How we think about the work Great product capabilities stay modular when they share one canonical picture of the business: catalog, mapping, pricing, availability, and vendor truth. That picture has to be accurate first. Then it has to move: scalable pipelines and serving paths so the same data can feed the business and product independently of where they may sit. Intelligence, both deterministic and learned, is what we build on top of that advantage. The quality of the intelligence is bounded by the quality and availability of the data underneath it. Your job is to make that stack a company strategy, not a collection of projects. Accuracy funds the platform. The platform funds intelligence. Intelligence that cannot name the data it depends on is not a bet we take. What you will own Company architecture for data and intelligence You will define the target-state architecture for the data estate and for applied AI, aligned to business priorities rather than tooling fashion. That includes how warehouse and lake, pipelines, streaming, and integrations fit together, and how model serving, retrieval, embeddings, prompt and model management, guardrails, and evaluation fit on top of that same governed foundation. You will establish source-of-truth models and a shared semantic picture of the business so fragmented sources converge into one reliable platform, which is the foundation AI depends on. You will set naming, modeling, lineage, and integration standards, and you will be the review that keeps new designs on that spine. You will make security, privacy, and compliance part of the design: classification, handling of sensitive data, access control, retention, and audit through every layer. Cost, performance, and reliability are first-class constraints in the architecture you defend, not optimizations someone else discovers later. AI strategy, standards, and the investment case You will own where AI creates durable advantages for the data function, includi