- In this role, you will lead the design, development, and deployment of advanced NLP models and generative AI applications to solve complex business problems
- You will be responsible for building and optimizing machine learning pipelines, particularly focusing on deep learning and generative models
- This role will involve working on various projects, including text analysis, language modeling, and model deployment in production environments
- You will also be instrumental in applying generative models for creative and business purposes, such as text generation and data augmentation
- Natural Language Processing (NLP):
- Lead the design and implementation of advanced NLP models for tasks such as text classification, named entity recognition (NER), topic modeling, sentiment analysis, and language translation
- Apply cutting-edge deep learning techniques like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), and transformer models (e.g., BERT, GPT) for complex NLP tasks
- Leverage pre-trained language models, word embeddings (Word2Vec, GloVe, FastText), and fine-tune them to meet custom business requirements
- Generative AI:
Apply Generative AI techniques, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models (e.g., GPT-3, T5), to develop solutions for text generation, data augmentation, and other creative use cases
- Explore innovative applications of Generative AI in content creation, including summarization, question generation, and dialogue systems
- Stay up-to-date with the latest advancements in Generative AI, and integrate them into existing pipelines to enhance model performance and functionality
- Collaborate with cross-functional teams to explore new business applications for generative models, such as synthetic data generation for model training or content generation for marketing
- Develop and optimize machine learning models using supervised learning techniques such as regression, classification, Support Vector Machines (SVMs), and decision trees
- Evaluate models using performance metrics such as accuracy, precision, recall, F1 score, and use cross-validation to ensure model robustness
- Unsupervised Learning:
- Use clustering algorithms, such as K-means and hierarchical clustering, and dimensionality reduction techniques like Principal Component Analysis (PCA) to uncover hidden patterns in data
- Implement anomaly detection models to identify rare events or outliers in datasets, supporting business intelligence and decision-making
- Lead the design and deployment of deep learning models using Convolutional Neural Networks (CNNs), RNNs, LSTMs, and transformers to handle complex tasks in both NLP and Generative AI
- Optimize and fine-tune deep learning architectures to improve accuracy, performance, and scalability of models in production
- Model Deployment and MLOps:
- Deploy machine learning models into production using cloud platforms such as Azure ML, ensuring scalability and performance
- Implement MLOps best practices, including CI/CD pipelines, model versioning, and automated retraining using tools like MLflow, Kubeflow, and Azure ML Pipelines
- Monitor models post-deployment, track model performance, identify drift, and retrain models as necessary to maintain their relevance and accuracy
