AI Platform Engineer

GiveCampus

2d•Remote

About The Position

GiveCampus is hiring an AI Platform Engineer to design, build, and operate the next generation of AI infrastructure supporting the company’s agents and workflows. The platform will provide shared company and Partner context and memory, identity and access controls, a registry of existing tools and connections, governance and monitoring, and a paved road for internal builders. The role focuses on architecture and engineering, with a product partner—the AI Transformation Lead—responsible for prioritization, stakeholder management, enablement, and adoption. In the first six months, the engineer will build shared context and memory, auditable agent permissions and credential visibility, a registry of existing systems, production evaluation and governance controls, monitoring and protections against prompt injection and data leaks, and templates and guardrails for builders. Over time, the role may expand to support end-to-end agent processes, greater agent autonomy, internal deployment platforms, model and cost strategy, and technical leadership for AI across GiveCampus. The position also includes occasional adjacent work, such as internal-tool front ends or adoption builds, when useful to the platform. The engineer will report to the AI Transformation Lead and collaborate with Engineering, Product, the AI Plumbers cross-team working group, and the AI Taskforce. GiveCampus is remote-first, with the role based anywhere in the United States. Team members are expected to attend multiple company-wide and team-specific onsites during the year. The company has a written, asynchronous culture and offers employees autonomy and direct access to leadership.

Requirements

Six or more years of software engineering experience, including meaningful experience owning platform, infrastructure, or backend systems in production.
Hands-on experience shipping LLM systems used by real users, including retrieval, tool use, evaluations, and MCP or similar integration patterns.
Strong Python skills.
Experience with cloud infrastructure; AWS is preferred.
Experience with authentication and observability.
A security mindset, including consideration of access, data boundaries, and failure modes before launch.
A track record of building internal platforms or shared services adopted by others.
Experience growing an early system into one on which a whole company depends.
Interest in growing into the technical lead for AI across the company.
Genuine interest in AI and ongoing learning, including keeping current with developments and trying new approaches.
Focus on business results rather than delivery alone.
Comfort working from requirements established with a product partner and exercising judgment to challenge them when the technical answer differs.
Clear written communication with technical and non-technical colleagues.

Nice To Haves

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.
Experience at a startup or small team as the first person to own a system.

Responsibilities

Design, build, and operate GiveCampus’s AI infrastructure platform.
Create shared company and Partner context and memory that can be used across agents.
Develop auditable identity and access controls for agents, including visibility into credentials and MCP connections.
Maintain a registry of existing AI tools and systems.
Build governance mechanisms, including pre-production evaluation gates, audit trails, monitoring, and protections against prompt injection and data leaks.
Make build-versus-buy decisions in partnership with Infrastructure.
Create templates and guardrails for internal Taskforce builders and measure impact by department.
Contribute to evolving the platform to support end-to-end agent processes, greater agent autonomy, internal deployment, and model and cost strategy as needs develop.
Own platform architecture and engineering decisions, and work from requirements set with a product partner while offering technical pushback when appropriate.
Collaborate with the AI Transformation Lead, Engineering, Product, AI Plumbers, and department-based AI Taskforce builders.
Pitch in on adjacent work, such as internal-tool front ends, adoption builds, or understanding business workflows, when it supports the platform.
Judge outcomes by business results, including time returned to teams, faster cycle times, and everyday tool adoption.