AI Platform Engineer - GiveCampus

GiveCampus is the world's leading fundraising platform for non-profit educational institutions. Trusted by millions of donors and 1,300+ colleges, universities, and K-12 schools, our mission is to help advance the quality, the affordability, and the accessibility of education.

Our purpose-driven team of 130+ is located in 30+ states across the US. We operate as a remote-first team with a 12,000 sq ft office in Washington, DC available for optional use, along with regular team meet-ups, partner visits, and retreats.

The Role

GiveCampus has spent the last 18 months shipping AI agents and apps into production, including codebase agents, RFP tools, support knowledge bots, and data integration assistants.

Now, we are building the next generation of AI Infrastructure at GiveCampus. As an AI Platform Engineer, you will design, build, and operate shared company and partner context layers, identity/access models, agent registries, cross-agent memory, and governance guardrails.

This is a platform role where you'll own technical architecture and engineering alongside an AI Transformation Lead who manages prioritization, stakeholder management, and product adoption.

What We're Driving

• Field teams spend time with customers instead of manual meeting prep and CRM updates.
• Every customer interaction is backed by context delivered automatically before conversations start.
• Managers act on AI-generated briefings rather than manual reporting.
• Finance, People, and Ops teams manage agents running recurring processes by exception.
• Standard enablement infrastructure, governed agent lifecycles, and department impact measurement.

What You'll Own

First 6 Months: Build the Foundation

• Shared context and memory: One source of truth for company/partner knowledge accessible across all agents.
• Identity and access: Auditable permissions, MCP connection management, and agent registries.
• Governance: Evaluation gates prior to production, audit trails, monitoring, and prompt injection/data leak protection.
• Paved road for builders: Templates, guardrails, and usage metrics across departments.

6 to 18 Months: Operating Layer

• End-to-end process agents, increased agent autonomy, internal deployment platforms, and cost/model optimization strategies.

Long Term

• Serve as the technical lead for AI across the company, setting architecture standards and partnering with Product and AI teams.

What You Bring

Must Have

• 6+ years of software engineering experience owning platform, infrastructure, or backend systems in production.
• Hands-on experience shipping production LLM systems (retrieval, tool use, evaluations, MCP or similar patterns).
• Strong Python skills, along with experience in cloud infrastructure (AWS preferred), authentication, and observability.
• Security mindset focused on access boundaries, data isolation, and failure modes.
• Proven track record of building internal platforms or shared services adopted by engineering/ops teams.
• Business-result focus (time saved, cycle times, active tool adoption).
• Clear written communication for technical and non-technical stakeholders.

Nice to Have

• Experience with agent orchestration, multi-agent systems, or governance tooling.
• Familiarity with Snowflake, dbt, or semantic layers.
• Experience working with Salesforce data and APIs.
• Early engineering experience at a startup or small team.

How We Work

• Pod structure: Report to the AI Transformation Lead, partnering with cross-functional AI Plumbers and departmental AI Taskforces.
• Remote-first async culture built around Notion, Slack, documented workflows, and direct autonomy.