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About This Role
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.
Role Summary 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.
What You’ll Do Agentic AI \& Workflow Automation
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.
Enterprise Knowledge Graph \& Ontology
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.
Model Engineering \& MLOps
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.
AI\-Driven Insights \& Analytics
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.
Conversational AI Development
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.
AI Safety, Governance \& Quality
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.
Collaboration \& Communication
Partner cross\-functionally with data scientists, platform engineers, product managers, and business stakeholders to align AI solutions with strategic objectives.
Translate complex technical concepts into clear narratives for non\-technical audiences; deliver compelling demos and briefings to senior leaders.
Document system architecture, model decisions, and operational runbooks to enable team knowledge\-sharing and long\-term maintainability.
What You’ll Need
5\+ years of professional experience in AI/ML engineering, with a proven portfolio of production deployments.
Demonstrable track record of shipping AI/ML systems in fast\-moving, ambiguous environments.
Prior experience working directly with product managers or business stakeholders — not just engineering teams.
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related quantitative field.
Technical Skills
Expert Python proficiency; deep familiarity with ML libraries (scikit\-learn, PyTorch, TensorFlow, HuggingFace) and the broader MLOps ecosystem.
Proven experience designing and deploying LLM\-based systems including RAG architectures, agent frameworks, and fine\-tuned models.
Hands\-on experience with knowledge graph construction, semantic modeling, and graph database technologies.
Understanding of ontology development: taxonomy design, entity relationships, and knowledge representation principles.
Experience with MLOps practices: model lifecycle management, containerization (Docker/Kubernetes), and CI/CD pipelines.
Familiarity with cloud AI/ML platforms (Azure ML, AWS SageMaker, GCP Vertex AI) and associated infrastructure services.
Communication \& Collaboration Skills
Ability to explain complex technical concepts to non\-technical stakeholders clearly and confidently.
Natural tendency to document, share, and standardize — you build things others can maintain and extend.
Comfort building and presenting demos that generate confidence and drive investment alignment.
Skills Snapshot
LLM \& Generative AI Agentic Workflows Knowledge Graph / Ontology
Model Fine\-Tuning RAG \& Vector Search MLOps \& Model Lifecycle
Python \& ML Libraries Cloud AI Platforms AI Safety \& Governance
Data Pipelines API Design \& Integration Stakeholder Communication
Why CBRE? At CBRE, we believe the future of commercial real estate will be built on data and technology. As a Senior AI/ML Engineer, you will be at the center of that transformation — building AI systems that reach real clients, drive measurable business outcomes, and redefine how a global enterprise operates.
Named a Fortune Most Admired Real Estate Company for 14 consecutive years.
Named a World’s Most Ethical Company by Ethisphere for 11 consecutive years.
Ranked \#3 on Barron’s Most Sustainable Company list.
Access to a global network of technology, data, and real estate expertise, with investment in AI and digital transformation at the highest levels of leadership.
Equal Employment Opportunity: CBRE is an equal opportunity employer that values diversity. We have a long\-standing commitment to providing equal employment opportunity to all qualified applicants regardless of race, color, religion, national origin, sex, sexual orientation, gender identity, pregnancy, age, citizenship, marital status, disability, veteran status, political belief, or any other basis protected by applicable law.
Candidate Accommodations: CBRE values the differences of all current and prospective employees and recognizes how every employee contributes to our company’s success. CBRE provides reasonable accommodations in job application procedures for individuals with disabilities. If you require assistance due to a disability in the application or recruitment process, please submit a request via email at recruitingaccommodations@cbre.com or via telephone at \+1 866 225 3099 (U.S.) and \+1 866 388 4346 (Canada).
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CBRE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CBRE AI Hiring
CBRE has 29 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Cleveland, OH, US, Baton Rouge, LA, US, Austin, TX, US. Compensation range: $62K - $210K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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