Job Title: AI Platform Engineer

Reports to: Lead AI Platform Engineer

Intelligent Audit is a fast-growing freight audit & business analytics technology company helping our customers become smarter shippers — shipping to their customers faster, cheaper, and with fewer delivery exceptions. We use big data, automation, and advanced analytics to help our customers remove inefficiencies in their global transportation spend.

The AI Platform Engineer is an entry-level member of the AI Platform & Enablement team, helping build, deploy and run AI tools used across Intelligent Audit. As the team’s first hire, you will work directly with its lead to develop AI agents and automations that write code, triage tickets, monitor systems and improve business workflows.

This role works closely with the team lead to deliver impactful AI solutions while developing the skills and experience needed to take on increasing responsibility as the team grows.

What You Will Do:

Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. The individual in this position will be expected, on a regular basis, to:

Build and improve AI agents and automations for teams such as Finance, Operations and Customer Support, working with the team lead on priorities and implementation.
Prototype AI workflows, test their results and use data to show where they save time or improve outcomes before wider rollout.
Develop Python applications, APIs and scripts that connect AI tools to internal systems through APIs and MCP servers.
Help build and improve the AI infrastructure behind IA’s automation, with guidance from the team lead.
Test and review AI-generated code and model output, and follow established security, privacy, access control and data-handling practices.
Document workflows, prompts and operating procedures, and share examples that help colleagues use AI tools effectively.
Build and manage multi-agent workflows and agent-assisted coding tasks, checking results and bringing questions or risks to the team lead.
Help deploy and run AI services using Docker, Linux and existing CI/CD pipelines, learning tools such as GitHub Actions and Jenkins with support from the team lead.
Help set up monitoring and alerts for the team’s services; read logs, investigate failures and implement reviewed fixes with guidance.
Work with colleagues to understand automation needs, communicate progress, manage competing tasks and improve solutions through feedback.

What You Will Bring:

Working skills in Python and some shell scripting, demonstrated through coursework, internships or personal projects.
Hands-on experience using LLMs or AI APIs to build a tool, project or hackathon solution.
An understanding of basic software development practices, including testing, Git, documentation and code review, and a willingness to deepen these skills.
Practical experience managing multi-agent workflows, including agent-assisted coding projects, with the ability to explain how you checked their results.
Comfort using Linux, Git and the command line.
Exposure to Docker, cloud services such as AWS or DigitalOcean, or CI/CD through classes, internships or personal projects.
Curiosity about repetitive business tasks and a willingness to learn how to turn them into useful, reliable automations.
The habit of validating AI output and checking that a solution works before calling it complete.
Problem-solving skills, attention to detail and willingness to ask for help and learn from feedback.
Clear communication, collaboration and the ability to manage multiple tasks with guidance on priorities.
Interest in learning AI tooling, deployment practices and reliable operation of production services.

Minimum Qualifications:

A bachelor’s degree in Computer Science, Software Engineering, Information Systems or a related field, recently earned or nearing completion.
Entry-level candidates are welcome; relevant coursework, internships and personal projects can demonstrate the skills listed above. No minimum years of professional experience are required.
A practical example of an AI or automation project you built, tested and can discuss, including what you learned and how you verified the outcome.
Willingness to build, deploy and support AI tools while learning directly from the team lead.

Preferred Qualifications:

SQL and basic database experience.
Exposure to infrastructure-as-code tools such as Ansible.
Familiarity with agent frameworks, prompt engineering or evaluating model output.
An internship or project involving DevOps, SRE or platform engineering.
Experience extending AI agents or automations beyond an initial prototype.

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Intelligent Audit

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