Applied AI Engineer

The Applied AI Engineer will design, prototype, and operationalize applied AI and analytics solutions aimed at automating work and improving decision-making and performance. The role partners with operational leaders and data engineering to move validated ideas into reliable production systems, while quantifying business impact.

What You’ll Do

• Design, build, and iterate on applied AI and machine learning solutions, including forecasting, classification, anomaly detection, NLP, and generative AI and LLM-based workflows, focused on solving operational problems.
• Independently create end-to-end proofs of concept to validate AI and analytics approaches quickly, including setting up the data, modeling, and lightweight infrastructure required to demonstrate value.
• Collaborate with data engineering to harden, scale, and operationalize solutions, including integration into operational systems, reliability and observability, and post-deployment iteration.
• Define success metrics, design offline and online evaluation methods, and quantify business impact.
• Build feedback loops to detect drift, regression, or misuse, and take action to address issues.
• Analyze operational data, workflows, and performance trends to identify where AI and automation can deliver measurable value and surface actionable insights for service delivery and efficiency.
• Partner with Geography or Vertical leadership and frontline operators to understand workflows, decision points, and operational constraints.
• Translate operational problems into well-scoped AI and analytics solutions, and communicate technical results back into clear, actionable guidance.
• Prepare, clean, and structure datasets for analytics and AI workflows.
• Engineer features and design retrieval strategies for LLM-based systems, coordinating with data engineering on upstream data quality and pipeline needs.
• Develop, test, and deploy analytics and AI solutions within the Databricks Lakehouse environment provided by data engineering.
• Apply software engineering practices such as version control, testing, code review, and modular design to enable easier hardening and maintenance.
• Pilot, refine, and support adoption of AI tools with field and operational teams.
• Iterate using user feedback, evaluation results, and evolving business needs so solutions deliver compounding value over time.
• Use practical judgment regarding model limitations, hallucinations, bias, privacy, and human-in-the-loop design to keep deployed solutions trustworthy and appropriate for the operational context.

Requirements

• Bachelor’s degree in Analytics, Data Science, Computer Science, Engineering, or a related field.
• 4–7 years of experience in analytics, data science, or AI/ML engineering, including at least 2 years building and deploying ML or AI solutions.
• Strong proficiency in Python and SQL, with experience writing maintainable, tested code beyond exploratory notebooks.
• Hands-on experience building applied AI or ML solutions (predictive models, NLP, or LLM-based applications), not only conceptual familiarity.
• Ability to build end-to-end proofs of concept independently, including data wrangling, modeling, and lightweight infrastructure to show value quickly.
• Experience partnering with data engineering or platform teams to take prototypes into production.
• Experience working with large datasets in modern analytics platforms such as Databricks.
• Proven ability to translate operational problems into analytical and AI approaches that deliver measurable business outcomes.
• Strong communication skills with non-technical stakeholders, including the ability to explain AI behavior, limitations, and results.

Technology Stack

• Python
• SQL
• Databricks
• Databricks Lakehouse
• MLflow

Preferred Qualifications

• Production experience with generative AI, LLM APIs (for example, OpenAI, Anthropic), RAG systems, or agentic workflows.
• Familiarity with MLOps tooling and practices such as MLflow, model registries, CI/CD for ML, and monitoring or observability.
• Experience designing evaluation frameworks for AI systems, including offline benchmarks and online experimentation.
• Experience in operational, services, or asset-heavy environments.
• Exposure to predictive modeling, time series analysis, or NLP in business contexts.
• Familiarity with Databricks Lakehouse concepts and collaborative analytics workflows.
• Track record of driving adoption of analytics or AI tools within business operations, including process and change-management considerations.
• Ability to work independently while managing multiple concurrent initiatives.

Location and Compensation

Location: Chicago, IL (onsite).
Salary: USD 85,000 to 100,000 per year.

Benefits

• Health, vision, and dental insurance
• Flexible spending accounts
• Health savings accounts
• Retirement savings plans
• Life and disability insurance programs
• Paid and unpaid time away from work
• Competitive pay