Domain Architect- AI/ML, Senior Specialist

. Assesses business requirements and the architectural framework of Artificial Intelligence/Machine Learning solutions to meet business needs, with internal and external clients and partners.
2. Builds the deployment plan for highly scalable and secure Artificial Intelligence platforms, applications, and systems that run in various cloud infrastructures and Artificial Intelligence devices successfully.
3. Applies architecture specification and documents technical and configuration for all users (customers, engineering teams, product teams); resolves Artificial Intelligence/Machine Learning solution deployment issues.
4. Builds relationships with trusted advisory stakeholder relationships, and acts as a SME for Artificial Intelligence/Machine Learning related products and services in specific verticals; staying current on the latest Artificial Intelligence/Machine Learning technologies and tools.
5. Applies moderately complex machine learning algorithms and technologies into organizational practices, such as regression models.
6. Analyzes the statistical analyses on business and processes using machine learning techniques to find out opportunities for business development and process improvement.
7. Performs the A/B testing tasks and initiatives on statistical models, machine learning algorithms and systems.
8. Optimizes statistical models continuously to achieve best performance of machine learning algorithms.
9. Validates business problems and needs; ensures appropriate AI technologies and tools are utilized to solve problems.
10. Pilots AI models and prototype applications; evaluates whether business challenges are addressed.
11. Performs code reviews, optimizes algorithms and models and conducts experiments to ensure the functionality and performance of AI products or solutions.
12. Participates in special projects and performs other duties as assigned.
13. Designs and develops advanced AI/ML models for applications including NLP, computer vision, and predictive analytics.
14. Leads model training and evaluation using large datasets, ensuring accuracy, data integrity, and model robustness.
15. Drives continuous experimentation, implements performance-enhancing optimizations, and identifies areas for architectural improvements in AI systems.
16. Champions efficient algorithmic design to improve the scalability, performance, and adaptability of machine learning applications.