Principal Enterprise Architect

First Student is North America’s leading provider of student transportation, helping millions of students get to and from school safely each day. Our technology teams build and support the operational, safety, customer, and employee-facing systems behind that work, including our HALO platform.   AI is an area of active investment for us. We are moving toward a world where we can build what we need quickly and safely, and this role exists to make sure that speed produces a coherent enterprise rather than a fragmented one.   About the Role   You own the architecture of First Student’s technology estate through its transition to an AI-native operating model — and you hold the call on what we build, what we buy, and what we do not build at all.   That last one is the point. Agentic development is about   to make   building software cheap. Cheap building without architecture produces an outcome most enterprises have already lived through once: thousands of Access databases, data scattered, unsecured, and poorly structured, and five custom CRMs because five lines of business each wanted their own. AI lets that happen faster and with more polish, which makes it more dangerous, not less. Your job is to prevent that while still letting us build whatever we genuinely need, with ease.   This is a binding-authority seat. Buy it, build it with agents, fold it into an existing product, or do not build it — your decision stands. There are no direct reports; the authority comes from the seat and from the quality of your reasoning, not from headcount.   You are not starting from   nothing . We have a draft set of architecture principles and standards, a current view of the value stream, a prototype AI-native SDLC, and an agent control plane and runtime already in early use.   Central IT does the majority of AI development today, with small pockets emerging in the field.   What is thin is the thinking on how the business can safely ideate with AI inside a container, and how   good ideas   move through a gate into real products. That is the widest gap between what exists and what we need, though it is a smaller share of the job than deciding what gets built at all and making the enterprise buildable AI-natively.   Our default lean is to buy the commodity and build the differentiator. SaaS for what is not specific to student transportation; build where our operating model is the advantage — routing, driver, operations, safety. Holding that line against “we could just build it now” is a real part of the job.   How   You’ll   Work   You   work   AI-native. You will not ship much code, but you   design with   frontier models and agents daily — authoring skills, rules, harnesses, and agentic workflows, and prototyping when a prototype is the fastest way to prove a design. This is not background familiarity. You cannot design agent-consumable architecture, interfaces, or knowledge if you do not live   in   the tools. Your output is architecture and decisions: patterns, reference designs, ADRs, standards, evals, and calls that hold.   Authority is the backstop, not the operating style. You set the pattern and keep the final call, but you earn agreement inside the design process rather than ruling on it afterward.   Teams should want you in the room early, not dread the review at the end.   You also own a class of   question   rather than a fixed list. As the organization goes AI-native, settled assumptions come loose: whether work management built for humans reading tickets survives contact with agentic delivery, where documentation has to live for agents to consume it natively, how systems and agents talk to each other, who owns which entity and where copies of data are allowed. Jira-or-no-Jira is only an example. These questions arrive continuously, and you are the person who resolves them.   A typical week may include:   Making buy, build, fold-in, or do-not-build calls on real   requests, and   writing down the   reasoning   so the decision holds the next time the question is asked.   Designing the integration and data   architecture   so new products plug into the estate rather than wire themselves into it.   Building out the safe harbor for business ideation and the gate that decides what happens to what comes out of it.   Working alongside the agentic delivery pod on a hard design, then carrying the resulting pattern outward to the teams that follow.   Resolving an open AI-native question that nobody has an established answer for yet.   Shaping   enterprise knowledge management with the Product team so that principles, standards, and product knowledge are usable by machines as well as people.   Getting ahead of a line of business that is about to stand   up   something the enterprise already has.   Key Responsibilities   Decide what gets built   Hold binding authority on buy, build with agents, fold into an existing product, or do not build — across the full enterprise estate.   Own the intake and decision process behind that authority: how requests surface, how they are assessed, and how quickly they get an answer.   Default to buying the commodity and building the   differentiator, and   hold that line as agentic delivery makes   building   look free.   Own build-versus-buy evaluation, technical trade-off   analysis, and   vendor and platform fit.   Kill redundancy before it exists. Preventing the fifth CRM is worth more than integrating it later.   Document decisions and their   reasoning   so calls are consistent, reviewable, and reusable rather than re-argued every quarter.   Architect the enterprise estate for coherence   Serve as the enterprise-wide architectural authority across the full estate: custom digital products (payroll, driver, operations, routing, scheduling, and customer- and employee-facing applications), the SaaS and ERP portfolio, cloud, data, and integration.   Settle system-of-record ownership and the