AI Platform Engineer

Title: AI Platform Engineer Location: United States

The role

GiveCampus has spent the last 18 months getting good at using AI. People use it every day, and we've shipped agents and apps that changed how work gets done: a codebase agent that answers questions no doc can, an RFP agent, a support knowledge bot, a feature request agent, and a data integrations assistant for our Partners.

Now, we are ready to take it to the next level. You'll design, build, and operate the next generation of AI Infrastructure at GiveCampus, including sources of company and Partner context, a clear model for who and what can access which data, a registry of what's already been built, memory that carries across agents, and the guardrails that let us move fast without losing track of what our agents are doing.

This is a platform role. You'll work alongside a product partner and co-builder (the AI Transformation Lead) who owns prioritization, stakeholder management, and adoption, so you can spend your time on architecture and engineering.

What we're driving

The AI Transformation team exists to change how GiveCampus operates, so that every team runs on connected workflows and agents by default, and output grows without headcount growing alongside it. Over the next year, that means:

• Field teams spend most of their time with customers instead of on meeting prep, research, CRM updates, and follow ups
• Every customer interaction is backed by context (school data, usage, relationship history, and peer benchmarks) delivered before the conversation starts
• Managers act on AI generated briefings and alerts instead of assembling reports by hand
• Finance, People, and Ops teams manage agents that run their recurring processes, stepping in only on exceptions and approvals
• The enablement infrastructure and operating model that make the change last: standard toolkits, a governed agent lifecycle, and measurement of impact by department

You'll contribute directly to every one of these outcomes by owning the platform that makes them possible. The agents doing this work are only as good as the context, access, and controls underneath them, and that's what you'll build.

What you'll own

You'll start by building the foundation that makes the outcomes above possible. After that, the role grows with the company and with AI itself, so treat the later stages as direction, not a fixed scope.

First 6 months: build the foundation

• Shared context and memory: one source of company and Partner knowledge that every agent reads from, so a change is made once and reaches every tool
• Identity and access: auditable permissions for agents, one view of credentials and MCP connections, and a registry of what's already built
• Governance: an evaluation gate before production, an audit trail, monitoring, and protection against prompt injection and data leaks, with build versus buy calls made alongside Infrastructure
• A paved road for builders: templates and guardrails for Taskforce builders, plus measurement of impact by department

6 to 18 months: from platform to operating layer

Things like agents that run whole processes end to end, more autonomy for agents that have proven themselves, an internal deployment platform for apps and agents, and model and cost strategy.

Long term: an AI native GiveCampus

Things like becoming the technical lead for AI across the company, setting the architecture standards internal AI work builds on, and being a thought partner to Product and AI Product, sharing foundations where it makes sense.

What this role is, and what it isn't

Your primary focus is the platform. You'll own the architecture and the engineering, and you'll make the technical calls. On a small team, you'll also pitch in on adjacent work when it helps: a front end for an internal tool, an adoption build, or getting deep on a business workflow to understand what the platform needs to support. That work is shared, not yours alone. The AI Transformation Lead owns product decisions, prioritization, enablement, and change management, and builds adoption tooling alongside you.

What you bring

Must have

• Six or more years of software engineering experience, including meaningful time owning platform, infrastructure, or backend systems in production
• Hands on experience shipping LLM systems with real users: retrieval, tool use, evaluations, and MCP or similar integration patterns
• Strong Python, plus experience with cloud infrastructure (AWS preferred), authentication, and observability
• A security mindset: you think about access, data boundaries, and failure modes before something ships
• A record of building internal platforms or shared services that other people adopted
• Experience growing a system from an early version into something a whole company depends on
• Appetite to grow into the technical lead for AI across the company
• Real excitement about AI and a drive to keep learning: you follow what's new, try things early, and stay current. You don't need to know everything yet, but AI moves fast enough that this role only works if you're engaged with it
• You judge your work by business results (hours given back to teams, faster cycle times, tools people use every day), not by what shipped
• Comfort working from requirements set with a product partner, and the judgment to push back when the technical answer is different
• Clear written communication with technical and non technical teammates

Nice to have

• Experience with agent orchestration, multi agent systems, or agent governance tooling
• Familiarity with Snowflake, dbt, or a semantic layer
• Experience with Salesforce data and APIs
• Time at a startup or small team where you were the first person to own a system

How we work

You'll report to the AI Transformation Lead. Together you form a small pod: they act as product lead and build for adoption, and you own the platform.

You'll partner closely with Engineering and Product through AI Plumbers, our cross team working group for AI architecture, agent orchestration, data access, and governance. You'll also work with our AI Taskforce, the builders in each department who will use what you build.

GiveCampus has a strong written culture built on Notion, Slack, and async work. We move quickly, document what we do, and give people real autonomy and direct access to leadership. We're remote first, and this role can be based anywhere in the US.

Why this role

• The groundwork is done. People already use AI every day and we have working agents in production. You're building the layer that makes them work together.
• Clear ownership. You own the platform, with a product partner who owns prioritization and adoption.
• Room to grow. You'll start with the foundation and grow into the technical lead for AI at GiveCampus as the work and the field change.
• Leadership investment. AI is a company priority, with executive sponsorship behind this work.
• A mission that matters. GiveCampus helps schools raise money to advance education.