Applied AI Engineer

Cushman & Wakefield•Cincinnati, OH

About The Position

The Applied AI Engineer is a hands-on builder who sits at the intersection of AI engineering, operational analytics, and business process expertise. This role designs, prototypes, and operationalizes AI and analytics solutions that automate work, sharpen decision-making, and measurably improve performance across the organization. The Applied AI Engineer embeds directly with operational leaders to translate real-world business challenges into working proofs of concept, and partners with data engineering to harden and scale the solutions that prove valuable. Reporting to the Director of Advanced Analytics & AI, this position is a critical execution role within the centralized Advanced Analytics & AI team. Success requires the ability to move quickly and independently during the prototype phase — building end-to-end without waiting for platform support — while also collaborating effectively with data engineering, operations, and business stakeholders to take proven solutions into production. Equal fluency in modern AI tooling, rigorous analytical thinking, and operational context is essential.

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, with at least 2 years building and deploying ML or AI solutions
Strong proficiency in Python and SQL, including writing maintainable, tested code beyond exploratory notebooks
Hands-on experience building applied AI or ML solutions — e.g., predictive models, NLP, or LLM-based applications — not only conceptual familiarity
Demonstrated ability to build end-to-end proofs of concept independently, including the data wrangling, modeling, and lightweight infrastructure needed 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
Demonstrated 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 make AI behavior, limitations, and results understandable

Nice To Haves

Production experience with generative AI, LLM APIs (e.g., OpenAI, Anthropic), RAG systems, or agentic workflows
Familiarity with MLOps tooling and practices (e.g., MLflow, model registries, CI/CD for ML, monitoring/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

Responsibilities

Design, build, and iterate on applied AI and machine learning solutions — including forecasting, classification, anomaly detection, NLP, and generative AI / LLM-based workflows — with a focus on solving concrete operational problems.
Independently build end-to-end POCs to validate AI and analytics ideas quickly — including standing up the data, modeling, and lightweight infrastructure needed to demonstrate value before committing to production investment.
Partner with the data engineering team to harden, scale, and operationalize solutions that prove out: integrating them into operational systems, ensuring they are reliable and observable, and supporting iteration once deployed. Data engineering owns the platform and production pipelines; this role owns the AI/analytics solution running on them.
Define success metrics, design offline and online evaluations, and quantify business impact. Build the feedback loops needed to detect drift, regression, or misuse and respond to them.
Analyze operational data, workflows, and performance trends to identify where AI and automation can deliver measurable value, and to surface actionable insights that support service delivery and efficiency.
Work directly with Geography or Vertical leadership and frontline operators to understand workflows, decision points, and constraints. Translate operational problems into well-scoped AI and analytics solutions — and translate technical results back into clear, actionable guidance.
Prepare, clean, and structure datasets for analytics and AI workflows. Engineer features, design retrieval strategies for LLM-based systems, and partner 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 the data engineering team. Apply software engineering practices — version control, testing, code review, modular design — so prototypes are easy to harden and solutions are easy to maintain.
Partner with field and operational teams to pilot, refine, and drive adoption of AI tools. Iterate based on user feedback, evaluation results, and evolving business needs so solutions deliver compounding value over time.
Apply practical judgment around model limitations, hallucinations, bias, privacy, and human-in-the-loop design so deployed solutions are trustworthy and appropriate for the operational context.