AI Platform Engineer

Intelligent Audit•Alpharetta, GA

2d•$70,000 - $90,000

About The Position

Intelligent Audit is a freight audit and business analytics technology company that uses big data, automation, and advanced analytics to help customers improve global transportation spending. The AI Platform Engineer is an entry-level member of the AI Platform & Enablement team and will help build, deploy, and operate AI tools used across the company. As the team’s first hire, this person will work directly with the team lead to develop AI agents and automations for tasks such as writing code, triaging tickets, monitoring systems, and improving business workflows. The role offers opportunities to deliver AI solutions while building skills and taking on increasing responsibility as the team grows. The position works with teams including Finance, Operations, and Customer Support to identify needs, prototype and validate AI workflows, and improve them based on feedback. It also contributes to Python applications and APIs, AI infrastructure, multi-agent workflows, service deployment and operations, monitoring, documentation, and responsible handling of security, privacy, access control, and data.

Requirements

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.
Understanding of basic software development practices, including testing, Git, documentation, and code review, and willingness to deepen these skills.
Practical experience managing multi-agent workflows, including agent-assisted coding projects, and ability to explain how results were checked.
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 willingness to learn how to turn them into useful, reliable automations.
Habit of validating AI output and checking that a solution works before considering it complete.
Problem-solving skills, attention to detail, and willingness to ask for help and learn from feedback.
Clear communication and collaboration skills, and ability to manage multiple tasks with guidance on priorities.
Interest in learning AI tooling, deployment practices, and reliable operation of production services.
Bachelor’s degree in Computer Science, Software Engineering, Information Systems, or a related field, recently earned or nearing completion.
A practical example of an AI or automation project built and tested that the candidate can discuss, including what they learned and how they verified the outcome.
Willingness to build, deploy, and support AI tools while learning directly from the team lead.
No minimum years of professional experience are required; entry-level candidates are welcome, and relevant coursework, internships, and personal projects can demonstrate the listed skills.

Nice To Haves

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.

Responsibilities

Build and improve AI agents and automations for Finance, Operations, Customer Support, and other teams, working with the team lead on priorities and implementation.
Prototype AI workflows, test their results, and use data to assess time savings or improved 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 infrastructure supporting the company’s AI automation, with guidance from the team lead.
Test and review AI-generated code and model output, following 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, check results, and raise questions or risks with the team lead.
Help deploy and operate AI services using Docker, Linux, and existing CI/CD pipelines, learning tools such as GitHub Actions and Jenkins with support.
Help set up service monitoring and alerts, 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.