Note: The job is a remote job and is open to candidates in USA. Cushman & Wakefield is a global leader in real estate services, and they are seeking an Applied AI Engineer to design, prototype, and operationalize AI and analytics solutions. This role focuses on automating work and improving decision-making through AI technologies while collaborating closely with operational leaders and the data engineering team.
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
Skills
- 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
- 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
Benefits
- Cushman & Wakefield also provides eligible employees with an opportunity to enroll in a variety of benefit programs, generally including health, vision, and dental insurance, flexible spending accounts, health savings accounts, retirement savings plans, life, and disability insurance programs, and paid and unpaid time away from work.
- In addition to a comprehensive benefits package, Cushman and Wakefield provide eligible employees with competitive pay, which may vary depending on eligibility factors such as geographic location, date of hire, total hours worked, job type, business line, and applicability of collective bargaining agreements.
Company Overview
Company H1B Sponsorship