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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. CBRE carefully considers multiple factors to determine compensation, including a candidate’s education, training, and experience. The minimum salary for the Senior Principal AI/ML Engineer position is $235,000.00 annually and the maximum salary for the Senior Principal AI/ML Engineer position is $255,000.00 annually. The compensation that is offered to a successful candidate will depend on the candidate’s skills, qualifications, and experience. Successful candidates will also be eligible for a discretionary bonus based on CBRE’s applicable benefit program. 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, disabili
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.