at least 2 years in AI/ML, 1 year in Gen AI/NLP development and implementation.
Strong knowledge of AI/ML algorithms and techniques, NLP, LLM techniques with a proven history of delivering business value
Experience with big data technologies (e.g., Hadoop, Spark) and cloud-based AI/ML services (Azure Open AI /AWS) preferred.
Deep understanding of Python and/or R programming languages, as well as experience in leveraging these tools to analyze complex datasets, build predictive models, and generate insights preferred
Bachelor's Degree in fields, such as Computer Science, Information Technology (required)
Day to Day:
Collaborate with cross-functional teams to gain a deep understanding of their Generative AI goals and aspirations) and leverage this knowledge to develop Generative AI solutions that align with their needs.
Drive the research, development, and implementation of generative AI models and algorithms, ensuring they meet the highest standards of performance, scalability, and reliability.
Drive the organization's GenAI strategy forward. This includes establishing frameworks and best practices for managing Gen AI solutions.
Support development and implementation of Gen AI security and governance policies to ensure that data is collected, managed, and used in a responsible and ethical manner. Manage and allocate resources effectively, ensuring the team has the necessary tools, infrastructure, and support to deliver high-quality AI solutions.
Provide mentorship and guidance to individuals and teams on the effective use of GenAI and other AI technologies, tools, and techniques to drive successful outcomes. This involves sharing best practices, providing coaching and feedback, and supporting the professional development of team members.
Additionally, actively foster a culture of continuous learning and development, encouraging the exploration of new approaches and technologies in the AI/ML field. Oversee on-going support for GenAI and other AI solution environments.
Monitor and evaluate the performance of generative AI models and algorithms, identifying areas for improvement and implementing necessary enhancements. Continuously evaluate new GenAI research, trends, technologies, tools, and platforms and their applications in the pharmaceutical industry.
Optimize and tune data infrastructure and systems for performance, reliability, and scalability.