[Hiring] Applied AI Engineer @Cushman & Wakefield

Role Description

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.

• Embed directly with operational leaders to translate real-world business challenges into working proofs of concept.
• Independently build end-to-end, including the data, modeling, and lightweight infrastructure needed to demonstrate value quickly.
• Partner with the data engineering team to harden, scale, and operationalize solutions for production use.
• Requires fluency in modern AI tooling, rigorous analytical thinking, and operational context.
• Combines technical depth with business fluency to work effectively across stakeholders, data, and code.

Qualifications

• 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.
• Demonstrated ability to build end-to-end proofs of concept independently.
• 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.

Requirements

• Design, build, and iterate on applied AI and machine learning solutions.
• Independently build end-to-end POCs to validate AI and analytics ideas quickly.
• Partner with the data engineering team to harden, scale, and operationalize solutions.
• Define success metrics, design evaluations, and quantify business impact.
• Analyze operational data, workflows, and performance trends.
• Work directly with leadership and frontline operators to understand workflows.
• Prepare, clean, and structure datasets for analytics and AI workflows.
• Develop, test, and deploy analytics and AI solutions within the Databricks Lakehouse environment.
• Partner with field and operational teams to pilot and drive adoption of AI tools.
• Apply practical judgment around model limitations, bias, and human-in-the-loop design.

Benefits

• Health, vision, and dental insurance.
• Flexible spending accounts and health savings accounts.
• Retirement savings plans.
• Life and disability insurance programs.
• Paid and unpaid time away from work.
• Competitive pay based on various eligibility factors.