Generative AI Engineer

Responsibilities:

• Integration and Collaboration: Collaborate with cross-functional teams to integrate generative AI solutions into existing workflow systems. Document and present complex findings and learnings in a clear and concise manner to a non-technical audience.
• Research and Innovation: Stay up-to-date on the latest advancements in generative AI technologies and methodologies such as Retrieval Augmented Generation (RAG), Vector Databases, frameworks such as langchain, etc.
• Algorithm Development: Design and develop algorithms for generative models using deep learning techniques, Natural Language Processing (NLP), Retrieval Augmented Generation (RAG) etc.
• Model Architecture Design: Create and fine-tune generative neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models.
• Data Management: Clean, label, and augment data for training and improving AI models.
• AI Alignment and Testing: Perform rigorous testing and validation of generative AI models to ensure their robustness and reliability. Address ethical considerations related to generative AI, such as bias, fairness, and transparency.

Skills: At least 5 years of experience in backend development and machine learning.

Technical Skills:

• Strong proficiency in Python and AI frameworks (TensorFlow, PyTorch).
• Proven experience in developing and implementing generative AI models, with a strong understanding of machine learning algorithms.
• Solid understanding data pre-processing techniques.
• Knowledge of cloud computing and AI deployment (Azure, AWS, Google Cloud) is a plus.

Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.