Is Your Data Ready for AI Implementations? |
For developing the multi-phased AI solution, the following is a typical structure of the project team members, deployed as needed in respective phases:
Consultant Roles* | Consultant Name | Location | |
Delivery Manager & Project Manager | Will be responsible for overall project planning, coordination, and management and will ensure that project objectives are met, timelines are adhered to, and resources are allocated effectively. | TBD | Seattle, WA, USA |
Lead Data Scientist | Will be recommending appropriate analytics approaches and AI/ML algorithms for solutions in each phase and guide the team in designing the technical architecture and framework for solutions, based on project requirements. | TBD | Cupertino, CA, USA |
Technical Lead AI Engineer | Will be designing and implementing the AI/ML solutions, leading the development of AI models, and ensuring successful integration and deployment of the AI solutions. | TBD | India |
Sr. Data Scientist | Will identify relevant data sources, perform data preprocessing, develop AI/ML models, and ensure the accuracy and quality of the models. | TBD | India |
Data Scientist – Shadow | Will perform data pre-processing and develop AI/ML models | TBD | India |
DevOps Engineer | Will set up and maintain the development and testing environments, as well as manage continuous integration and deployment processes. to ensure smooth operations and efficient deployment of the AI/ML solution. | TBD | India |
Data Engineer | Will be responsible for data ingestion, data transformation, data quality assurance, and ensure the availability and reliability of data for analysis and model training throughout the project lifecycle. | TBD | India |
Quality Assurance Engineers (QA) | Will be responsible for testing the solution, identifying, and reporting any issues or bugs, and ensuring that the solution meets the specified requirements and quality standards. | TBD | Seattle (WA) |
Table of Content
Project Management Approaches and Philosophy