AI/ML Enterprise Architect

GBIT (Global Bridge InfoTech Inc) is seeking a Senior Enterprise Architect specializing in Artificial Intelligence to join their global team based in Richardson, Texas. The role focuses on driving AI-based transformation projects and involves collaboration with various teams to design and implement AI/ML solutions that deliver business value. Responsibilities: 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 Qualifications: Minimum bachelor's degree qualification, with strong quantitative background; 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; 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 of experience with data science tools and frameworks; Recent experience in Generative AI, Large Language Models (LLMs), Agentic AI platforms and solutions; Excellent written, verbal and presentation communications skills; 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; Ability to approach design with timeliness, cost and quality factors in mind; Risk identification and mitigation, quantification and qualification non-functional and functional requirements; Driven digital transformation, roadmaps, target operating models, cost reduction, performance improvement Required Skills: AI/ML Solution Architecture, AI/ML Prototyping and Experimentation, AWS AI Services, Azure AI Services, GCP AI Services, Cloud-native Data Storage, Cloud-native Compute, Cloud-native Networking, Cloud-native Security, Statistical Concepts, Data Exploration, Feature Engineering, Data Preprocessing, Data Science Tools and Frameworks, Generative AI, Large Language Models (LLMs), Agentic AI, Communication, Presentation, Influencing