Gen AI Architect

Responsibilities
• Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Build various ML Models within the Model guidelines and framework.
• Consults with peers for guidance, as needed.
• Translates business requirements into specific analytical questions, build ML Models and present model outcomes to non-technical business colleagues.
• Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions
• Stay current with emerging trends and technologies in data quality management, data profiling, data cleansing tools and AI/ML.
• Collaborate with data governance teams to ensure compliance with regulatory requirements and industry and legal standards related to data quality and privacy.
• Able to identify GenAI use cases given in various business scenarios and come up with possible solutions.
• Familiar with various GenAl technologies, Prompt Engineering, RAG etc
Skills & Qualifications
• 10 to 12 years of relevant experience, and 6+ years of experience in data science, machine learning, quantitative analytics (Mathematics, Statistics or Operational Research etc) roles
• Master’s degree in computer science, Statistics, or a related field (Mathematics, Operational Research, Data Science)
• Experience in Building and validating statistical, machine learning, and other advanced analytics models.
• Experience in Regression (multiple, Logistic etc), Classification (Decision Tree, Random Forest, XGBoost etc) and Time series Forecasting models (ARIMA), Segmentation, NLP, Deep Learning and Graph Analytics.
• Experience in Data Mining, Python, R, SQL, and familiarity with ML technologies
• Experience in using ML Libraries.
• Working Experience in Domino Data Lab, AWS Sagemaker, Snowflake are a plus
• Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop