Enterprise Architect - AI/ML

We are looking for a Senior Enterprise Architect, in Artificial Intelligence (AI) to join the global team based in Richardson, Texas. A hands‑on AI/ML Enterprise Architect role demands a blend of deep technical expertise, architectural design skills, and practical implementation capabilities. You'll help solution and drive AI based transformation projects at CBRE. This area is a critical component to the enablement of our digital products. This role involves working across multiple functions, building strong relationships with various teams such as Product Management, Technology, Data and Business Transformation teams. In addition to internal constituents, you may also work with customers and partners to understand the market requirements and to support the business, so excellent communication (written and verbal) and a passion for creating customer centric user experience is vital. What You'll Do Collaborate with business and technology teams in the identification, design, prototyping and selection of AI/ML solutions needed to deliver business value. Architect and help build enterprise solutions which include components across the Artificial Intelligence spectrum such as Machine Learning, generative AI, Deep‑Learning, Virtual Assistants and Cognitive Services (e.g., Vision/Image, Textual/Language processing). Create and articulate future state AI vision via all the relevant architecture & design artifacts. Help validate these proposed future state architectures with hands‑on prototypes and MVPs. Evaluate market solutions and evolving platforms for AI/ML, and assess fitment to CBRE demands, ROI analysis, readiness and develop enterprise recommendations. Assist in the development of roadmaps for AI & machine learning and other advanced analytics areas (e.g., NLP, NLQ, LLMs, Agentic AI, MCP, A2A, neural networks, computer vision, ML platforms, open‑source libraries, event‑driven stream processing, data lakes). Continuous outside‑in scanning, to facilitate continuous learning, hands‑on understanding, & adoption of AI Solutions Architecture industry standard methodologies & techniques. Rapid Proof‑of‑Concept (PoC) Development: Independently and quickly build functional prototypes and PoCs to test new AI models, algorithms, and approaches to solve specific business problems. Model Experimentation: Conduct hands‑on testing with various machine learning models, including fine‑tuning large language models (LLMs), training custom models, and evaluating their performance using appropriate metrics. Data Exploration and Preparation: Write scripts and use tools to perform data exploration, cleansing, and feature engineering on diverse datasets to prepare them for prototyping and model training. Technical Validation: Quickly set up and test new tools, APIs, and frameworks to validate their capabilities and suitability for larger‑scale implementation. Architectural Mentorship: Offering support and mentorship to data science and engineering teams throughout the development, deployment, and operationalization of AI models. System Integration: Design and facilitate the integration of AI/ML models into existing enterprise applications, APIs, and business workflows. Success Criteria To be successful in this role you will have: Have an excellent technical and commercial understanding in the creation of technology solutions and outcomes for customers. Ability to learn emerging technologies and to apply these technologies to tackle business problems. Strong desire to experiment with and learn new technologies and stay aligned with the latest community developments in AI/ML Ops/Engineering and cloud native solutions. Excellent written, verbal and presentation communications skills; you will often find yourself in situations where you have to describe problem statements, solution options, your analysis and recommendations to a mix of technical and non‑technical and use strong influencing skills to gain consensus and an agreed way forward. Ability to approach design with timeliness, cost and quality factors in mind. Risk identification and mitigation, quantification and qualification non‑functional and functional requirements. What You'll Need To perform this job optimally, an individual will need to perform each crucial duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Minimum bachelor’s degree qualification, with strong quantitative background (STEM education background (Science, Technology, Engineering & Mathematics) AND/OR an MBA preferred). Deep knowledge of AI/ML tech across AWS/Azure and GCP. Understanding of cloud‑native services for data storage, compute, networking, and security. Proven grasp of statistical concepts (e.g., hypothesis testing, probability distributions, regression analysis). Experience with data exploration, feature engineering, and data preprocessing techniques. Technical Proficiency, Including 5+ years of hands‑on AI/ML solution architecture and hands‑on prototyping and experimentation. 5+ years of AWS/Azure data, Analytics and AI services. 3+ years hands‑on experience with data science tools and frameworks. Recent experience in Generative AI, Large Language Models (LLMs), Agentic AI platforms and solutions. #J-18808-Ljbffr GBIT (Global Bridge InfoTech Inc)