Senior Founding Engineer

present of AI; Arkansas; On-site; USD 90,000 - 130,000; Full time

Job summary

present of AI is seeking a hands-on builder who ships working systems from data integrations to an agent layer and the underlying schema. A strong candidate has helped carry a startup through growth and built systems that survived it.

Breadth matters more than narrow depth: backend Python, some frontend, data modelling, reliability work and design tradeoffs. In this role you’ll work on agent harnesses, tool interfaces, retrieval and memory, applying to messy distributor and retailer data.

Qualifications

• Hands-on builder who ships working systems from data integrations to an agent layer.
• Startup growth experience is valued and systems survive scale.
• Breadth over depth: backend Python, frontend, data modelling, reliability, and design tradeoffs.

Responsibilities

• Build and maintain agent harnesses, tool interfaces, and memory.
• Ensure reliability, observability, and measurable evaluations.
• Design data schemas and ensure data lineage.

Skills

Backend Python

Frontend development

Data modelling

Reliability

Observability

Systems design

Job description

This role asks for a hands on builder who can take a vague problem and ship working systems alone, from data integrations and orchestration to an agent layer and the schema behind it. A strong candidate has already helped carry a startup through a growth stage and personally built the systems that survived it. Breadth matters more than narrow depth: backend Python, some frontend, data modelling, reliability work and a clear sense of design tradeoffs.

How it fits the AI era

The product is built around agents that find sales gaps and propose or take actions, so much of the work is making those agents reliable, observable and improvable through evaluations. AI tools speed up coding, but judgment about system design, data meaning and failure handling stays with the engineer. When something breaks, the person is expected to find the real cause rather than blame the agent.

How it builds AI experience

Working here means hands on time with agent harnesses, tool interfaces, retrieval, evaluations and memory, applied to messy distributor and retailer data for consumer brands. That gives practical grounding in making agents dependable on real business data rather than demos. To prepare, building a small agent that queries a governed dataset and measuring its accuracy would map closely to the work.

Worth showing

• Built agent tools and evals
• Designed data schemas and lineage
• Owned reliability and observability
• Scaled systems at an early startup

This is our own read, written from the employer's posting and the company's Spotlight. The employer's posting is the source of truth for the role.

About Corvera

Corvera provides an artificial intelligence sales and operations platform that turns distributor data into prioritized daily store actions, per the company.