Principal Machine Learning Engineer

Cushman & Wakefield

2d•$178,500 - $210,000

About The Position

We are seeking a Principal ML Engineer to build and maintain artificial intelligence and machine learning systems for our business. This role combines deep technical execution with broad influence: tackling the most complex machine learning problems, designing and implementing data architectures/models, MLOps and LLMOps, and monitoring and optimization as key software, while raising the bar for engineering excellence across the Data Organization. Reporting to the Senior Director, Data Science & AI, the Principal ML Engineer is expected to move fluidly across project/product teams — embedding in projects for days or months at a time to unblock, accelerate, and uplift delivery. They will mentor other engineers at every level, push standards for technical excellence, and help establish the ML engineering, and architectural principles that guide our work. The primary platform is Databricks and the Azure cloud ecosystem, with the expectation of evaluating and adopting additional data technologies as the platform evolves.

Requirements

10+ years building software or machine learning systems, including 6+ years shipping and operating machine learning in production — served, monitored, and maintained after launch.
Prior experience as the senior-most or near-senior-most engineer on a production ML or data platform, with responsibility for architectural decisions others had to live with.
A track record of setting technical standards that outlasted your own involvement, and of leading through influence without formal authority.
Strong hands-on Python and SQL, and the software engineering fundamentals that keep ML systems maintainable at scale — version control, automated testing, code review, modular design — plus the discipline to enforce them across teams.
Expert hands-on proficiency with Databricks (Spark, Lakeflow Declarative Pipelines, Delta Lake, Lakebase/Postgres, Unity Catalog, Model Serving) and the Azure data ecosystem. Deep experience on a comparable lakehouse platform will be considered, with the expectation of becoming effective on Databricks quickly.
Experience with MLflow or an equivalent for experiment tracking, model registry, tracing, and reproducibility.
At least one year building agentic systems that reached production use, or equivalent demonstrated work. You should be able to walk us through a system you built: how it was orchestrated, how you evaluated it, what guardrails you put around it, how it failed, and what standard you set for whoever built the next one.
Proven ability to mentor engineers at every level and materially raise the technical standard of the teams you work with.
Familiarity with modern machine learning frameworks and a strong instinct for reusable, scalable engineering patterns.

Nice To Haves

Experience establishing an ML or AI platform capability in an organisation that did not previously have one.
Experience embedding across multiple teams, geographies and time zones as a principal-level technical contributor.
Familiarity with CI/CD and infrastructure-as-code tooling for data pipelines using Azure DevOps, Databricks Declarative Automation Bundles (DABs), GitHub Actions, Terraform, or equivalent.
Good understanding of data governance, metadata management, and cataloguing in enterprise environments.
Exposure to model serving and inference at scale — containers, REST endpoints, latency and throughput targets — or to feature stores, retrieval pipelines, or LLM cost-attribution tooling.
Model Context Protocol (MCP) or comparable tool-integration standards.
Commercial real estate, professional services, or another domain where the data is messy, entities are inconsistently defined, and subject matter experts are the source of truth.

Responsibilities

Demonstrable experience shaping the end-to-end architecture, development, and deployment of AI, including machine learning, GenAI, and Agentic models that are tailored to business use cases.
Design and develop agentic AI systems — including multi-agent orchestration, tool-use, evaluation, and autonomous task-execution pipelines — to automate complex enterprise workflows.
Enforce model development standards encompassing data preprocessing, feature engineering, model selection, hyperparameter tuning, evaluation, and documentation.
Partner with data engineering teams to ensure robust, scalable, and high-quality data pipelines that support model training and inference.
Implement the organization's MLOps and LLMOps capabilities, including CI/CD pipelines for data wrangling, feature engineering, model training, evaluation, deployment, and monitoring.
Help define and enforce standards for model versioning, experiment tracking, reproducibility, and model registry management.
Implement robust model monitoring frameworks to detect performance degradation, data drift, concept drift, and bias in production systems, with automated alerting and retraining triggers.
Implement AI governance frameworks, including policies for model risk management, fairness, explainability, privacy, and security.
Champion sound AI principles across the global business, ensuring that human oversight and accountability are embedded in every stage of the AI development lifecycle.