Interested in this AI/ML Engineer role at Bigge Crane and Rigging?
Apply Now →Skills & Technologies
About This Role
Introduction
Bigge Crane and Rigging 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
Benefits
- Competitive pay and a matching 401(k) plan
- Vacation, Company Holidays, and Sick Days
- Flexible spending accounts/Health Savings Account
- Reimbursement plan for the company Bring Your Own Device (BYOD) Policy
Bigge provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, military or veteran status.
Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Bigge Crane and Rigging, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,000.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Bigge Crane and Rigging AI Hiring
Bigge Crane and Rigging has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Houston, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.