About the Role:
As a CBRE Director of Cloud AI Engineering, you will build and lead the team responsible for designing, developing, and shipping Cloud & AI-powered products and engineering platforms across CBRE's Cloud Platform Services organization. You own the Cloud AI function end-to-end: the people, the product roadmap, the engineering execution, and the architectural decisions that determine how AI capabilities are built, deployed, and scaled across CBRE's enterprise cloud environment.
This job is part of the Cloud Software Engineering job function. Your team builds the Cloud and AI products that CBRE's internal engineering and operations teams rely on - including Cloud Assistant and agentic AI tooling - and the underlying engineering platform that makes those products possible. You work at the intersection of cloud infrastructure, and applied AI/ML, partnering closely with Cloud FinOps, Cloud Engineering, and business technology leaders to define what gets built and ensure it ships.
What You'll Do:
- Team Leadership & People Management
- Provide formal leadership to the Cloud AI engineering team. Hire, develop, and retain top AI and software engineering talent. Conduct performance evaluations, set development plans, and coach engineers at all levels.
- Coordinate and manage the team's day-to-day activities. Establish work schedules, assign priorities, and cross-train staff to build depth and resilience across the function.
- Mentor and develop engineers into senior and principal contributors. Create a team culture of engineering rigor, iterative delivery, and continuous learning.
- Set and track team and department milestones. Hold the team accountable to delivery commitments while removing the blockers that slow them down.
Cloud AI Product Strategy & Roadmap
- Own the what the team builds, in what order, and why. Set priorities based on business impact, engineering feasibility, and alignment with Cloud Platform Services' strategic direction.
- Partner with cloud engineering, FinOps, and business technology leaders to identify where Cloud & AI product roadmaps: define AI tooling can remove bottlenecks, improve decision quality, and accelerate time to insight across the enterprise.
- Drive build vs. buy decisions on Cloud and AI capabilities - evaluating cloud provider AI services (Azure OpenAI, AWS Bedrock, Google Vertex AI) against internal development options and recommending the right approach for each use case.
- Own the enterprise AI self-service roadmap: set priorities, manage delivery, and measure impact against clear, quantifiable business outcomes.
- Assist with the development of goals and initiatives to guide the department's course. Evaluate processes and procedures, identify gaps, and make recommendations to senior leadership.
AI Engineering & Product Delivery
- Lead the engineering delivery of next-generation agentic AI tools that give CBRE's internal teams direct access to data, analysis, and recommendations through natural language and automated workflows.
- Build and ship production-grade AI systems: from LLM-powered interfaces and agentic automation pipelines to ML model serving infrastructure and AI observability tooling.
- Design and maintain a platform insights layer that surfaces usage patterns, performance signals, and optimization opportunities across CBRE's enterprise technology stack.
- Ensure AI products integrate cleanly with existing internal platforms including AIDP (Automated Infrastructure Deployment Platform), ECMP (Enterprise Container Management Platform), and Innovation Studio.
- Significantly improve and change existing methods, processes, and standards within the AI engineering discipline - establishing best practices that raise the bar across Cloud Platform Services.
Cloud AI Architecture & Delivery Strategy
- Define the architectural principles and technical standards for how AI is built on CBRE's cloud infrastructure. Ensure systems are secure, scalable, observable, and cost-efficient by design.
- Define intelligent data strategies that connect cloud infrastructure, enterprise data assets, and front-line business needs into a coherent, scalable architecture - enabling AI products to draw on reliable, governed data.
- Partner with Cloud FinOps to build AI-driven cost intelligence capabilities: anomaly detection, spend forecasting, rightsizing recommendations, and natural language cost querying.
- Ensure responsible AI practices are embedded in how the team builds: governance, data privacy, model explainability, bias evaluation, and audit readiness.
Cross-Functional Leadership & Stakeholder Management
- Maintain relationships with senior technology leaders, business segment partners, cloud vendors, and external AI ecosystem partners. Negotiate with vendors and external partners of divergent interests to reach the right outcomes for CBRE.
- Lead by example and model behaviors consistent with CBRE RISE values. Persuade managers and colleagues to take action while being guided by the organization's functional business plans.
- Ensure business operations and AI initiatives are implemented based on established procedures and within compliance and security constraints. Ensure managers implement company initiatives and policies correctly.
- Apply a robust knowledge of AI/ML, cloud engineering, and enterprise software disciplines, and translate that knowledge into decisions that impact departmental and cross-functional performance.
- Identify and solve multi-dimensional, complex, operational, and organizational problems, leveraging the appropriate resources within or outside the department.
What You'll Need:
Education & Experience
- Bachelor's Degree preferred with 8-12 years of relevant experience. In lieu of a degree, a combination of experience and education will be considered.
- 5+ years leading and managing software or AI engineering teams, including hiring, performance management, coaching, and organizational development.
- Demonstrated track record of shipping production AI products - LLM-based applications, agentic systems, ML model serving, or AI-powered SaaS - at enterprise scale.
- Deep hands-on background in cloud-native software engineering; ability to engage credibly at the architecture and code level with senior engineers on the team.
Technical Skills
- Applied AI/ML engineering: strong command of LLM APIs (Azure OpenAI, OpenAI, Gemini, Bedrock), agentic AI frameworks (LangChain, AutoGen, or equivalent), prompt engineering, RAG architectures, and AI evaluation methods.
- Cloud platforms: multi-cloud proficiency across Azure, AWS, and GCP; experience building AI workloads on cloud-native services and integrating with cloud provider AI/ML offerings.
- Software engineering: strong foundation in Python and modern software architecture; experience with microservices, APIs, containerization (Docker/Kubernetes), CI/CD, and infrastructure-as-code.
- Data architecture: ability to define and evaluate data strategies that support AI product needs - including data pipelines, vector stores, embedding strategies, and governed data access patterns.
- MLOps: experience establishing model deployment, versioning, monitoring, and retraining practices in production AI systems.
Leadership & Competencies
- Experience in staffing, selection, training, development, coaching, mentoring, measuring, appraising, and rewarding performance and retention is preferred.
- Ability to lead the exchange of sensitive, complicated, and difficult information, convey performance expectations, and handle problems.
- Leadership skills to set, manage, and achieve targets with a direct impact on multiple departmental results within a function.
- Strong executive communication: able to translate AI strategy and engineering progress into clear narratives for senior leadership, finance partners, and non-technical business stakeholders.
- Expert organizational skills and an advanced inquisitive mindset. In-depth knowledge of Microsoft Office products including Word, Excel, and Outlook
