Hands-on experience: Practical application of listed technologies is crucial in key areas relevant to the role, including:
RAG (Retrieval Augmented Generation)
Function Calling/Tools
Langchain
Vector databases
Clear articulation of contributions: Candidates should be able to explain their involvement and impact in specific projects.
Deep understanding of GenAI concepts: Beyond surface-level knowledge, a strong grasp of core concepts, including non-functional requirements (safety filter, data security, prompt injection, grounding), is essential.
Familiarity with emerging trends: Keeping up to date with the latest advancements in GenAI is highly desirable.