Applied AI Engineer

Position at Parts Town

See What We're All About

As the fastest-growing distributor of restaurant equipment, HVAC and residential appliance parts, we like to do things a little differently. First, you need to understand and demonstrate our Core Values with safety being your first priority. That's key. But we're also looking for unique enthusiasm, high integrity, courage to embrace change...and if you know a few jokes, that puts you on the top of our list!

Do you have a genius-level knowledge of original equipment manufacturer parts? If not, no problem! We're more interested in passionate people with fresh ideas from different backgrounds. That's what keeps us at the top of our game. We're proud that our workplace has been recognized for its growth and innovation on the Inc. 5000 list 15 years in a row and the Crain's Fast 50 list ten times.

We are honored to be voted by our Chicagoland team as a Chicago Tribune Top Workplace for the last four years.

If you're ready to roll up your sleeves, go above and beyond and put your ambition to work, all while having some fun, let's chat --

• Parts Town Pride -- check out our virtual tour and culture!
• Quarterly profit-sharing bonus
• Hybrid Work schedule
• Team member appreciation events and recognition programs
• Volunteer opportunities
• Casual dress code
• On demand pay options:
  Access your pay as you earn it, to cover unexpected or even everyday expenses
• All the traditional benefits like health insurance, 401k/401k match, employee assistance programs and time away -- don't worry, we've got you covered.

The Applied AI Engineer will advance internal AI capabilities by owning and improving high‑value, production‑ready AI solutions that are reliable, maintainable, and integrated into core business workflows. Working at the intersection of LLMs, machine learning, and modern software engineering, the role partners closely with AI, Business, and Enterprise Data teams to build enterprise‑grade systems across cloud and enterprise platforms. Success requires translating technical outcomes into clear business value while helping shape long‑term AI strategy and deployment across internal and customer‑facing experiences.

• Design, develop, and deploy LLM and ML based AI solutions into production
• Build and maintain RAG pipelines, prompt orchestration workflows, and AI-driven automation systems
• Develop scalable inference services, APIs, and integration layers
• Investigate and resolve complex system and data challenges across AI pipelines, diagnosing root causes and implementing robust solutions
• Define and implement evaluation frameworks to assess AI performance, reliability, and business impact
• Integrate AI systems with cloud data platforms and enterprise applications
• Partner with business stakeholders to translate operational challenges into structured, measurable AI solutions
• Contribute to architectural decisions that ensure scalability, maintainability, and clear system boundaries
• Uphold strong engineering standards, documentation practices, and reproducibility across AI systems

• You have strong problem‑solving skills, intellectual curiosity, and a builder mindset to break down ambiguous problems and design, prototype, and iterate on AI or software solutions
• You have strong Python skills with solid software engineering fundamentals and experience deploying ML/AI systems into production
• You have experience building APIs or service‑based architecture and working with LLM‑based systems (RAG, prompt orchestration, evaluation frameworks)
• You have experience working with cloud platforms and operating effectively in fast‑evolving, ambiguous environments with a strong sense of ownership
• You can balance execution speed with engineering discipline and communicate technical concepts clearly to business stakeholders

• You have experience with enterprise data platforms, semantic modeling, ontology‑driven or knowledge‑based systems
• You have experience integrating AI solutions into operational business workflows
• You are familiar with monitoring, observability, and MLOps practices supporting production AI systems

Our AI team…