About this role
The Applied AI Engineer designs, prototypes, and operationalizes AI and analytics solutions for operational problems. Reporting to the Director of Advanced Analytics & AI, the role works directly with operational leaders and frontline teams to turn business challenges into proofs of concept, then partners with data engineering to bring successful solutions into production. The position is listed as on-site in Chicago, Illinois, USA.
Responsibilities
- Build and iterate on applied AI and machine learning solutions, including forecasting, classification, anomaly detection, NLP, and generative AI or LLM-based workflows. Independently assemble the data, models, and lightweight infrastructure needed to demonstrate value quickly.
- Own the AI and analytics solutions running on platforms and production pipelines owned by data engineering. Help integrate solutions into operational systems, maintain reliability and observability, and improve them after deployment.
- Define success metrics; conduct offline and online evaluations; measure business impact; and establish feedback loops to detect drift, regression, or misuse.
- Analyze operational data and workflows, prepare and clean datasets, engineer features, and design retrieval strategies for LLM systems. Develop and test solutions in the Databricks Lakehouse environment using version control, testing, code review, and modular design.
- Work with business leaders and operators to scope problems, communicate results and limitations, pilot tools, and drive adoption. Account for hallucinations, bias, privacy, model limitations, and human-in-the-loop design.
Required qualifications
A bachelor’s degree in Analytics, Data Science, Computer Science, Engineering, or a related field; 4–7 years in analytics, data science, or AI/ML engineering, including at least 2 years building and deploying ML or AI solutions. Strong Python and SQL skills, hands-on applied AI or ML development, independent end-to-end prototyping, experience with large datasets and modern analytics platforms such as Databricks, production collaboration with data engineering or platform teams, and clear communication with nontechnical stakeholders are required.
Preferred qualifications
Production experience with generative AI, LLM APIs, RAG, or agentic workflows; MLOps practices and tooling; AI evaluation frameworks; operational, services, or asset-heavy environments; predictive modeling, time series analysis, or NLP; Databricks Lakehouse workflows; and experience driving adoption and managing concurrent initiatives.
The stated compensation range is $85,000.00–$100,000.00. Eligible employees may enroll in health, vision, dental, retirement, insurance, spending-account, and paid or unpaid time-away programs.