GenAI Engineer_NuSummit(Formerly Named NSE-IT)

Responsibilities:

1. Design and Develop AI-Powered Products:

• Collaborate with cross-functional teams to design and develop AI-powered products that meet business requirements.
• Use foundational language models (e.g., BERT, RoBERTa, XLNet) and Transformer Architecture to build and train custom models for various applications.
• Experiment with different architectures, hyperparameters, and training techniques to optimize model performance.

2. Develop and Train Custom Models:

o Develop and train custom models using large-scale datasets and state-of-theart techniques (e.g., transfer learning, few-shot learning).

• Fine-tune pre-trained language models for specific downstream tasks (e.g., sentiment analysis, question answering).
• Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.

3. Experimentation and Failure Analysis:

• Design and execute experiments to validate hypotheses about AI-powered product performance.
• Analyze results, identify areas for improvement, and propose changes to the product or solution.
• Collaborate with the team to implement design changes and iterate on the product development process. 4. Scaling Solutions:
• Develop and deploy scalable solutions that can handle large volumes of data and traffic.
• Optimize model performance for production environments using techniques such as model pruning, knowledge distillation, or quantization.
• Collaborate with infrastructure teams to ensure seamless deployment and maintenance of AI-powered products.

5. Knowledge Sharing and Collaboration:

• Share expertise and knowledge with colleagues through regular meetups, workshops, and blog posts.
• Participate in code reviews, provide feedback on peers' work, and contribute to open-source projects.
• Collaborate with other teams (e.g., data science, product management) to ensure alignment and effective communication.
• Technical Skills: o Strong foundation in deep learning, natural language processing, and computer vision. o Experience with foundational language models (e.g., BERT, RoBERTa, XLNet) and their applications. o Familiarity with popular deep learning frameworks (e.g., TensorFlow, PyTorch). o Knowledge of large-scale data storage and processing systems (e.g., Hadoop, Spark).