Principal Machine Learning Engineer (AI)

Location: Remote within the United States, associated with Creve Coeur, Missouri

Salary: $178,500–$210,000 annually

Company

Cushman & Wakefield operates in commercial real estate and professional services, using data and AI to support business operations.

What you will do

• Design, develop, deploy, and maintain AI, machine learning, generative AI, and agentic models for business use cases.
• Build agentic AI systems with multi-agent orchestration, tool use, evaluation, guardrails, and autonomous task-execution pipelines.
• Establish MLOps and LLMOps capabilities covering data preparation, feature engineering, training, evaluation, deployment, monitoring, and retraining.
• Implement model versioning, experiment tracking, reproducibility, model registries, and monitoring for performance degradation, drift, and bias.
• Define AI governance practices for model risk, fairness, explainability, privacy, security, human oversight, and accountability.
• Mentor engineers, embed across project and product teams, and set architectural and engineering standards for the Data Organization.

Requirements

• 10+ years building software or machine learning systems, including 6+ years shipping and operating machine learning systems in production.
• Experience serving as the senior-most or near-senior-most engineer on a production ML or data platform and making architectural decisions.
• Strong hands-on Python and SQL skills, with expertise in software engineering practices including version control, automated testing, code review, and modular design.
• Expertise with Databricks and the Azure data ecosystem, including Spark, Delta Lake, Unity Catalog, Model Serving, and related platform capabilities.
• Experience with MLflow or an equivalent for experiment tracking, model registries, tracing, and reproducibility.
• At least one year of production experience building agentic systems, plus the ability to mentor engineers and lead through influence.

Nice to have

• Experience establishing an ML or AI platform where one did not previously exist.
• Experience working across multiple teams, geographies, and time zones as a principal-level contributor.
• Experience with Azure DevOps, Databricks Declarative Automation Bundles, GitHub Actions, Terraform, or equivalent infrastructure-as-code tooling.
• Knowledge of data governance, metadata management, cataloguing, model serving at scale, feature stores, retrieval pipelines, LLM cost attribution, or Model Context Protocol.
• Experience in commercial real estate, professional services, or domains with messy data and inconsistent entity definitions.

Culture & Benefits

• Remote work arrangement for the United States-based role.
• Health, vision, and dental insurance options.
• Flexible spending accounts and health savings accounts.
• Retirement savings, life, and disability insurance programs.
• Paid and unpaid time away from work.