Artificial Intelligence (Generative AI) Engineer
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Job Description
About The Company:
Beehive AI offers a generative AI platform specializing in analyzing an organization’s unique qualitative data. This platform utilizes self-learning language models, validated by human experts, and incorporates built-in statistical analysis. It’s designed to enable research and insights leaders to swiftly, accurately, and securely analyze qualitative data, combining it with quantitative data for more robust customer insights. Beehive AI integrates with various organizational insights programs, eliminating data silos and creating a consistent analytical approach. Unlike traditional ML/NLP tools needing manual setup and maintenance, or generic LLM-based generative AI solutions posing data security risks, Beehive AI employs organization-specific generative AI and LLMs. This ensures safe and scalable qualitative data analysis, allowing for the combination with quantitative data and the generation of accurate, real-time business and customer insights.
About The Position: Artificial Intelligence (Generative AI) Engineer
As a Generative AI Engineer at Beehive AI, you’ll be responsible for designing, developing, experimenting with, and maintaining various components of the company’s algorithms and LLMs. This role demands independent work across the entire development lifecycle, from researching and experimenting with generative AI solutions to their implementation and production deployment.
Responsibilities:
• Designing, developing, experimenting, and maintaining components of Beehive AI’s algorithms and LLMs.
• Working independently on the full development cycle, from research and experimentation to implementation and productionalization.
Requirements:
• Advanced Degree: PhD in Computer Science, AI, Linguistics, Applied Physics, or a related field with a focus on AI and natural language processing.
• LLM and PyTorch Expertise: Extensive experience with large language models and strong proficiency in PyTorch.
• Analytical & Problem-Solving Skills: Ability to tackle complex challenges in model training and optimization.
• Communication & Collaboration: Excellent communication skills to convey technical information and collaborate effectively with cross-functional teams.
• Innovation & Continuous Learning: A passion for staying current with the latest advancements in AI and machine learning.
• Python Proficiency: Strong Python programming skills.
