Applied AI Engineer

Parts Town•Addison, IL

1d•$133,953 - $157,542•Hybrid

About The Position

The Applied AI Engineer advances internal AI capabilities by owning and improving high-value, production-ready AI solutions that are reliable, maintainable, and integrated into core business workflows. Working across LLMs, machine learning, and modern software engineering, this role partners with AI, Business, and Enterprise Data teams to build enterprise-grade systems across cloud and enterprise platforms. The engineer translates technical outcomes into clear business value and helps shape long-term AI strategy and deployment across internal and customer-facing experiences. The AI team focuses on building meaningful solutions and solving real problems. It values curiosity, strong problem-solving, and people who enjoy working through ambiguous challenges, while balancing focused work with optional opportunities to reset and connect.

Requirements

Strong problem-solving skills, intellectual curiosity, and a builder mindset for breaking down ambiguous problems and designing, prototyping, and iterating on AI or software solutions.
Strong Python skills and solid software engineering fundamentals.
Experience deploying ML/AI systems into production.
Experience building APIs or service-based architecture.
Experience working with LLM-based systems, including RAG, prompt orchestration, and evaluation frameworks.
Experience working with cloud platforms.
Ability to operate effectively in fast-evolving, ambiguous environments with a strong sense of ownership.
Ability to balance execution speed with engineering discipline and communicate technical concepts clearly to business stakeholders.

Nice To Haves

Experience with enterprise data platforms, semantic modeling, ontology-driven systems, or knowledge-based systems.
Experience integrating AI solutions into operational business workflows.
Familiarity with monitoring, observability, and MLOps practices supporting production AI systems.

Responsibilities

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 by 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.