Junior AI Engineer (Hybrid)

Job Description

Job Description

Introduction

Bigge Crane and Rigging Co. has been elevating America since 1916. With over 1,800 cranes and a coast-to-coast presence, we buy, sell, rent, operate, and maintain one of the largest, most advanced crane fleets in the country. We’ve played a role in building some of America’s most iconic landmarks and earned a reputation for doing it right—with performance, precision, and accountability.

Why You Should Join Bigge

Bigge is an AI-first crane company. It is how the work gets done here, and we stood up a dedicated AI Engineering department to make sure it keeps going that way.

We are power users. Our people work with frontier AI tools every day, and our own engineers build the connections that plug those tools into the systems this company actually runs on: fleet, sales, scheduling, documents. That layer is ours. So is our data. We buy software when buying is the right answer and we build when it is not, and we are not precious about either.

We expect less off-the-shelf software in our future and more built by Bigge for Bigge’s needs.

You will not wait a quarter for a committee to approve something. Real problems, 1,800 cranes worth of real operating data, and a team small enough that what you build carries your name.

Position Overview

The Junior AI Engineer is an early-career builder who develops and ships AI solutions under the direction of the Director of AI Engineering. This role contributes to MCP connectors against Bigge systems of record, builds and extends AI agents and agentic workflows, and writes the tests and evaluations that validate them, working within established architecture and review processes rather than owning them.

This is the development seat on a three-level ladder: Associate AI Engineer, Junior AI Engineer, AI Engineer. The expectation is growth into the AI Engineer role. We hire for demonstrated ability to ship, not years on a resume.

The ideal candidate is technically capable, curious, and coachable. You will work daily with the Director of AI Engineering and alongside the AI Engineer, with increasing independence as your work earns it.

Key Responsibilities

• Build and extend MCP connectors against Bigge systems of record, including WorkPro (Bigge’s internal asset management lifecycle platform), D365, and Microsoft 365, under established patterns and review
• Implement components of AI agents and agentic workflows designed with the Director of AI Engineering
• Write and maintain tests, evaluation sets, and regression checks for systems before they ship and after every change
• Integrate LLM capability into internal applications and APIs under direction
• Attend process discovery sessions with business units to observe workflow mapping and contribute to process documentation
• Assist in collecting performance baselines and metrics for departmental initiatives
• Adhere to responsible-AI practices, data governance standards, and IT security requirements
• Write clean, tested, documented code. Participate in code review as author and grow into reviewing others

Qualifications and Skills

• Bachelor’s degree in Computer Science, Engineering, Data Science, or equivalent experience
• 1 to 3 years of professional experience, with shipped work you can show us: a deployed project, a production contribution, a working system you built. We screen on what you have built, not how long you have been employed
• Working proficiency in Python or TypeScript, and willingness to become proficient in both. You will use both
• Experience building against real APIs: authentication, error handling, and reading documentation that is sometimes wrong
• Hands-on experience with LLM APIs and prompt engineering, through work, coursework, or personal projects
• Strong communication and problem-solving abilities, with the willingness to work directly with field and office personnel
• Coachable, organized, and able to learn quickly in a fast-moving environment
• Have a current valid driver’s license along with a clean driving record

Preferred Skills

• Exposure to the Model Context Protocol or other tool-calling architectures
• Next.js, React, and Tailwind
• Docker and cloud deployment of any kind
• Local model serving or fine-tuning experiments, at any scale
• Prior work or internship in construction, heavy equipment, logistics, or industrial operations