Responsibilities:
- Design, develop, and optimize LLMs and NLP models for various applications, such as text classification, named entity recognition, sentiment analysis, and question answering.
- Fine-tune pre-trained LLMs (e.g., GPT, BERT, T5) on specific tasks using domain-specific data.
- Work closely with cross-functional teams to integrate NLP solutions into products and services.
- Continuously monitor, evaluate, and improve the performance of deployed models, addressing issues such as bias, fairness, and accuracy.
- Contribute to the development and maintenance of NLP pipelines and workflows, including data preprocessing, model training, and inference.
- Design and develop retrieval algorithms and models, including TF-IDF, BM25, and vector-based methods, to enhance search relevance and efficiency.
- Design, develop, and optimize hybrid workflows integrating LLMs with retrieval algorithms.
Requirements:
- Bachelor’s degree in Computer Science, Data Science, Applied Mathematics, or a related field. A Master’s degree is a plus.
- 2-5 years of experience in machine learning and NLP, with experience in LLMs and retrieval systems.
- Knowledge of transformer architectures and experience implementing them in NLP tasks.
- Experience with data preprocessing techniques for text data, including tokenization, stemming, lemmatization, and handling imbalanced datasets.
- Strong understanding of deep learning frameworks like TensorFlow or PyTorch.
- Hands-on experience with NLP libraries and frameworks such as Hugging Face Transformers, spaCy, NLTK, or similar.
- Experience with search engine frameworks such as Elasticsearch, Solr, or Lucene.
- Knowledge of indexing techniques and data structures used in search engines.
- Experience with query parsing, term weighting, and relevance feedback mechanisms.
- Familiarity with cloud platforms (AWS, Google Cloud, Azure) for training and deploying LLMs.
- Excellent problem-solving skills with the ability to innovate and experiment
- Collaborative mindset with the ability to work effectively in cross-functional teams.