Model Development and Optimization : Design, build, and deploy NLP models, including transformer models (e.g., BERT, GPT, T5) and other SOTA architectures, as well as traditional machine learning algorithms (e.g., SVMs, Logistic Regression) for specific applications.
Data Processing and Feature Engineering : Develop robust pipelines for text preprocessing, feature extraction, and data augmentation for structured and unstructured data.
Model Fine-Tuning and Transfer Learning : Fine-tune large language models for specific applications, leveraging transfer learning techniques, domain adaptation, and a mix of deep learning and traditional ML models.
Performance Optimization : Optimize model performance for scalability and latency, applying techniques such as quantization, ONNX formats etc.
Research and Innovation : Stay updated with the latest research in NLP, Deep Learning, and Generative AI, applying innovative solutions and techniques (e.g., RAG applications, Prompt engineering, Self-supervised learning).
Stakeholder Communication : Collaborate with stakeholders to gather requirements, conduct due diligence, and communicate project updates effectively, ensuring alignment between technical solutions and business goals.
Evaluation and Testing : Establish metrics, benchmarks, and methodologies for model evaluation, including cross-validation, and error analysis, ensuring models meet accuracy, fairness, and reliability standards.
Deployment and Monitoring : Oversee the deployment of NLP models in production, ensuring seamless integration, model monitoring, and retraining processes.
(ref:hirist.tech)
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