Interested in this AI/ML Engineer role at Tradebe?
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About This Role
Tradebe is a group of industrial businesses with the commitment of creating a more sustainable planet and making significant contributions to human wellbeing. In the US, we are leaders focused on recycling and circular economy, managing all different environmental liabilities in a sustainable way.
What will you do? Make an impact!
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As an AI Solutions Engineer, you will accelerate business value delivery by rapidly turning ideas into AI\-enabled applications, workflow automation solutions, and modern digital capabilities. Working closely with business leaders, operational teams, IT, and Global Digital Innovation teams, you will design, prototype, and deploy practical AI\-powered solutions that improve operational efficiency, simplify workflows, and modernize legacy processes.
This role is highly hands\-on and execution\-focused. You will rapidly translate ambiguous business problems into scoped experiments, MVPs, and scalable production\-ready solutions. You will work across the full solution lifecycle: from idea and prototyping through deployment, operationalization, monitoring, and continuous improvement.
Key Responsibilities
Rapid Prototyping \& MVP Delivery
- Develop and deliver rapid AI prototypes and demo\-ready MVPs that validate business value quickly and support fast iteration with business stakeholders.
- Translate ambiguous business challenges into working solutions, and scalable applications.
AI Application Development
- Build, deploy, and maintain AI\-enabled applications, workflow automation solutions, and user\-facing tools that improve operational efficiency and business processes.
- Develop end\-to\-end solutions leveraging modern AI technologies including large language models (LLMs), retrieval\-augmented generation (RAG), APIs, orchestration frameworks, and AI\-assisted automation.
Workflow Automation \& Legacy Modernization
- Replace or simplify legacy internal applications with fit\-for\-purpose solutions while implementing AI\-assisted workflow automation and modern user experiences.
- Design solutions that reduce manual effort, simplify complex workflows, and improve usability for business users.
Enterprise Integration \& Operationalization
- Integrate AI solutions with enterprise applications, operational platforms, and data environments while ensuring solutions are scalable, secure, maintainable, and production\-ready.
- Establish observability, monitoring, governance, and operational controls to support long\-term reliability and supportability.
Cross\-Functional Collaboration
- Partner with business teams, BRMs, IT, architecture, security, and Global Digital Innovation teams to align technical solutions with operational needs and governance standards.
- Serve as a technical translator between business stakeholders and technical teams to accelerate solution adoption and execution.
AI Governance \& Responsible AI
- Support AI governance initiatives including approved tools and models, vendor evaluation, data privacy, security controls, and value realization tracking.
- Ensure AI deployments align with company security, compliance, and operational standards.
Production Readiness \& Continuous Improvement
- Ensure solutions meet operational requirements for accuracy, resiliency, maintainability, usability, and cost efficiency.
- Continuously iterate and improve deployed solutions based on business feedback and operational performance.
Training \& Capability Building
- Upskill IT teams and business stakeholders through hands\-on collaboration, reusable templates, practical examples, and build\-together approaches.
- Help establish reusable patterns and best practices that enable broader AI adoption across the organization.
Do you have what it takes?
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- Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent hands\-on experience preferred.
- Experience:
+ 3\+ years of hands\-on software development experience across backend, frontend, and data layers.
+ 1\+ years of experience delivering applied AI solutions such as Generative AI, Retrieval\-Augmented Generation (RAG), AI\-assisted automation, intelligent search, or workflow orchestration solutions.
+ Experience building and integrating solutions using APIs, AI frameworks, orchestration platforms, and enterprise data sources.
+ Experience modernizing or replacing legacy/internal applications with scalable, maintainable solutions.
+ Experience with cloud platforms, low\-code/no\-code automation tools, orchestration frameworks, observability tooling, or enterprise integrations is a plus.
- Skills:
+ Proven full\-stack development capability building APIs, user\-facing applications, workflow automation solutions, and data\-driven platforms.
+ Proven ability to rapidly prototype, pilot, and iterate solutions with business stakeholders from idea through production deployment.
+ Strong communication and collaboration skills with the ability to translate technical concepts into practical business outcomes.
+ Ability to thrive in a fast\-paced environment while balancing multiple priorities and rapidly evolving technologies.
What’s in for you?
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Why Tradebe is Right for You
- Competitive pay and benefits
- Student loan repayment assistance
- Generous vacation and sick plans
- Medical (including telehealth), dental and vision
- 401k Retirement match
- Flexible spending accounts (FSA)
- Health savings accounts (HSA)
- Agency paid, basic life and AD\&D insurance
- Career ladders, professional development, and promotion opportunities
- Leadership opportunities
- Great work environment and culture
- And MORE!
Ready to make a difference? Apply now!
\#TeamTradebe \#SustainableCareers \#TradebeJobs
The annual salary for this position ranges from $100,000\-$125,000/year depending on factors such as your location, experience, skills, and qualifications. Please note that the top end of the range is not guaranteed. This range is provided to offer transparency and should not be interpreted as a guaranteed offer. Final compensation will be determined through a thoughtful assessment of your background and fit for the role.
In addition to base salary, this position is eligible for bonus potential, in line with our company’s bonus policies.
If this offer does not match your expectations, but you would like to develop your career in a company that promotes circular economy and sustainability, register on our Career Page, and don't miss out on new job opportunities!
*Tradebe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, physical or mental disability, genetic factors, military/veteran status or other characteristics protected by law*
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Nearest Major Market: Chicago
Salary Context
This $100K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Tradebe, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($112K) sits 38% below the category median. Disclosed range: $100K to $125K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tradebe AI Hiring
Tradebe has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $125K - $125K.
Location Context
AI roles in Chicago pay a median of $201,225 across 312 tracked positions.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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