Job Overview

Huemn is seeking a talented Generative AI Engineer to join our dynamic team in Hyderabad. This full-time, junior-level position requires a professional with 1 to 3 years of experience in the field. As a Generative AI Engineer, you will be instrumental in advancing our AI capabilities, especially in leveraging natural language processing and large language models to revolutionize studio management for photographers around the world.

Qualifications and Skills

• Proficiency in Python and experience with PyTorch for building and deploying machine learning models.
• Knowledge of deep learning techniques and their application in real-world scenarios to solve complex problems.
• Hands-on experience with TensorFlow for implementing machine intelligence in scalable systems.
• In-depth understanding of machine learning algorithms and neural networks for developing intelligent applications.
• Expertise in natural language processing (Mandatory skill) for effective communication and interaction with systems.
• Strong proficiency in working with large language models (LLM) (Mandatory skill) to enhance cognitive computing capabilities.
• Experience in retrieval-augmented generation (RAG) (Mandatory skill) for innovative AI-driven solutions.
• Ability to collaborate with cross-functional teams and contribute to innovative projects in a fast-paced environment.

Roles and Responsibilities

• Develop and optimize natural language processing applications to enhance our studio management platform.
• Implement and maintain large language models to advance Huemn's AI-driven offerings.
• Utilize retrieval-augmented generation techniques to innovate new AI functionalities.
• Collaborate with team members to integrate AI solutions into existing systems and workflows.
• Conduct research and stay updated on the latest advancements in generative AI technologies.
• Ensure the scalability and efficiency of AI models in production environments.
• Participate in code reviews, feedback sessions, and contribute to engineering best practices.
• Document processes, algorithms, and code for future reference and knowledge sharing.