Senior Principal AI​/ML Engineer

About CBRE Data & Technology

CBRE is the world’s largest commercial real estate services and investment firm. Our Data & Technology organization sits at the intersection of real estate expertise and digital innovation, building the platforms, data products, and AI capabilities that give CBRE and its clients a decisive competitive edge. We are transforming how CBRE operates — embedding advanced AI directly into the business to accelerate delivery, surface insight, and turn product vision into measurable client and revenue impact.

The Senior AI/ML Engineer is a hands‑on technical practitioner responsible for designing, building, and operationalizing production‑grade AI and machine learning systems that power enterprise intelligence. This is not a research role — it is an engineering role. You own deliverables end‑to‑end.

This position sits at the center of our most strategic AI initiatives: advancing agentic workflows, developing the enterprise knowledge graph and its underlying ontology, and delivering AI‑driven insights that support account intelligence and portfolio analytics programs. You will build reusable, scalable AI capabilities that replace fragmented experiments with durable platform assets.

You are equally at home designing a knowledge graph schema in the morning and shipping fine‑tuned model evaluations in the afternoon. You write production code, make principled architecture decisions, and communicate clearly with both engineers and business stakeholders.

• Design and implement agentic AI frameworks including multi‑agent orchestration, tool‑calling pipelines, and autonomous task execution systems.

• Build and optimize RAG (Retrieval‑Augmented Generation) pipelines, prompt chaining workflows, and memory systems that operate reliably at enterprise scale.

• Integrate LLM‑powered agents with internal APIs, databases, and business systems to automate complex, knowledge‑intensive workflows.

• Contribute to the design and maintenance of the enterprise knowledge graph, including schema design, entity resolution, and relationship modeling.

• Lead ontology development efforts — defining concepts, hierarchies, and taxonomies that structure enterprise data within the knowledge platform.

• Integrate semantic models and graph databases with conversational AI and search systems to improve contextual retrieval and reasoning.

• Design, train, evaluate, and deploy ML models for predictive analytics, classification, anomaly detection, and optimization use cases.

• Apply domain‑specific fine‑tuning techniques to align large language models with enterprise knowledge and workflows.

• Build and maintain ML pipelines using MLOps tooling — ensuring reproducibility, model versioning, drift monitoring, and CI/CD integration.

• Analyze large, complex datasets to surface actionable trends, patterns, and signals using statistical and machine learning methods.

• Develop intelligent summarization, extraction, and insight‑generation capabilities that convert unstructured data into structured business intelligence.

• Support account intelligence and portfolio analytics initiatives by building AI‑powered features that surface risks, opportunities, and recommendations.

• Architect and fine‑tune intelligent virtual assistants and multi‑turn dialogue systems using transformer‑based LLMs and enterprise knowledge sources.

• Design conversation flows, intent hierarchies, and fallback strategies that ensure reliable, high‑quality performance across diverse user inputs.

• Identify and mitigate risks in AI/ML systems including hallucination, bias, concept drift, and adversarial vulnerabilities.

• Implement evaluation frameworks, guardrails, and observability tooling to monitor model quality in production environments.

• Ensure all AI systems adhere to responsible AI principles and organizational data governance standards.

• Partner cross‑functionally with data scientists, platform engineers, product managers, and business stakeholders to…