Responsibilities :
Understand the requirements from the business and translate it into an appropriate technical requirement.
Responsible for successful delivery of MLOps solutions and services in client consulting environments
Define key business problems to be solved formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client.
Assist clients with operationalization metrics to track performance of ML Models
Help team with ML Pipelines from creation to execution
Guide team to debug on issues with pipeline failures
Understand and take requirements on Operationalization of ML Models from Data Scientist
Engage with Business / Stakeholders with status update on progress of development and issue fix
Setup Standards related to Coding, Pipelines and Documentation
Research on new topics, services and enhancements in Cloud Technologies
Additional Responsibilities:
Good knowledge on software configuration management systems
Strong business acumen, strategy and cross-industry thought leadership
Awareness of latest technologies and Industry trends
Logical thinking and problem solving skills along with an ability to collaborate
Two or three industry domain knowledge
Understanding of the financial processes for various types of projects and the various pricing models available
Client Interfacing skills
Knowledge of SDLC and agile methodologies
Project and Team management
Technical and Professional Requirements:
Educational Requirements (any of the following):
Bachelor of Engineering/Bachelor of Technology in any stream with consistent academic track record.
Bachelor's degree in a quantitative discipline (e.g., Operation Research, Statistics, Mathematics) or significant relevant coursework with consistent academic track record. Additional Academic Qualification (good to have):
Masters in any area related to Science, Operation Research, Mathematics and Statistics with consistent academic track record.
PhD in any stream.
