Machine Learning (ML)Responsibilities
• 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.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Full Stack Developer - Averlon