AI/ML Engineer

AWS SageMaker Graph Database Hallucinations Memory Systems Responsible AI Data Governance Experimentation Production Code Knowledge Graph Computer Science Business Systems Machine Learning Containerization Docker (Software) Unstructured Data Anomaly Detection Conversational AI Workflow Management Software Versioning Multi-Agent Systems Lifecycle Management Predictive Analytics Systems Architecture Digital Transformation Azure Machine Learning Commercial Real Estate Artificial Intelligence Large Language Modeling Knowledge Representation Hugging Face (NLP Framework) Python (Programming Language) Ethical Standards And Conduct Scikit-Learn (Python Package) Intelligent Virtual Assistant Retrieval Augmented Generation Generative Artificial Intelligence PyTorch (Machine Learning Library) MLOps (Machine Learning Operations) Transformer (Machine Learning Model) Artificial Intelligence Infrastructure Application Programming Interface (API)

This role is STARs-friendly: Skilled Through Alternative Routes.

35% STARs in role., 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., Richardson, TXOn-Site Leadership Mentorship Innovation Algorithms Mitigation Data Lakes Agentic AI Mathematics Prototyping Real Estate Data Science Data Storage Communication Data Analysis Presentations Deep Learning Quantification Cost Reduction Microsoft Azure Decision Making Experimentation Computer Vision Machine Learning Stream Processing Influencing Skills Advanced Analytics Product Management Data Preprocessing Amazon Web Services Feature Engineering Regression Analysis Technology Solutions Architectural Design Emerging Technologies Solution Architecture Cloud-Native Computing Digital Transformation Commercial Real Estate Continuous Development Artificial Intelligence Enterprise Architecture Large Language Modeling Business Transformation Event-Driven Programming Probability Distribution Artificial Neural Networks Verbal Communication Skills Market Requirements Documents Statistical Hypothesis Testing Microsoft Certified Professional Large Language Model Fine-Tuning Generative Artificial Intelligence Proof Of Concept (POC) Development Certified Broadcast Radio Engineer Master Of Business Administration (MBA) Application Programming Interface (API) +0 AWS Practice Architect AI/ML TEKsystems Dallas, TXRemote JIRA Sales Linux CI/CD DevOps Gitlab Github Ansible Jenkins Presales Scripting Terraform Bitbucket Operations Leadership Consulting Management Automation Innovation Resilience Kubernetes Artifactory API Gateway Cloud Design Communication Microservices Sales Support Cloud Services Load Balancing Cloud Migration Project Closure Amazon DynamoDB Customer Service Project Planning Computer Science Scope Management Machine Learning AWS CodePipeline Code Refactoring Project Delivery Office Procedures Docker (Software) Analytical Skills Solution Delivery Service Offerings Windows PowerShell Business Valuation Problem Management Amazon Web Services Employee Onboarding Resource Management Service Improvement Atlassian Confluence Serverless Computing Strategic Objectives System Administration Solution Architecture Business Intelligence Technology Strategies Information Technology Cloud-Native Computing Full Stack Development Operational Excellence Technical Requirements Internal Communications Artificial Intelligence Project Risk Management Technical Communication Business Transformation Research And Development Cloud-Native Development Bash (Scripting Language) C# (Programming Language) Critical Illness Insurance Java (Programming Language) Verbal Communication Skills Team Performance Management Node.js (Javascript Library) Infrastructure as Code (IaC) Service Improvement Planning Cloud Computing Architecture Python (Programming Language) Virtual Private Networks (VPN) Project Initiation Documentation Application Lifecycle Management Key Performance Indicators (KPIs) JavaScript (Programming Language) Chef (Configuration Management Tool) Puppet (Configuration Management Tool)

Tooling Research Taxonomy Fallback AI Safety Vertex AI Leadership Governance Innovation Kubernetes TensorFlow API Design Ontologies Agentic AI Scalability Real Estate Data Science Decisiveness Tool Calling Communication, * 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

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., 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.