Data Scientist
3 weeks ago
Role : Data Scientist
Job Responsibilities :
LLM Architecture :
- Good understanding of the architecture underlying large language models, such as Transformer-based models and their variants.
- Design and implement deep learning model architectures using PyTorch.
Language Model Training and Fine-Tuning :
- Experience in training large-scale language models from scratch, as well as fine-tuning pre-trained models on domain data.
Data Preprocessing for NLP :
- Skilled in preprocessing textual data, including tokenization, stemming, lemmatization, and handling of different text encoding.
Transfer Learning and Adaptation :
- Proficiency in applying transfer learning techniques to adapt existing LLMs to new languages, domains, or specific business needs.
Data Annotation and Evaluation :
- Skills in designing and implementing data annotation strategies for training LLMs and evaluating their performance using appropriate metrics.
Scalability and Deployment :
- Experience in scaling LLMs for production environments, ensuring efficiency and robustness in deployment.
Model Training, Optimization, and Evaluation :
- Evaluate the performance of PyTorch models using appropriate metrics and techniques like cross-validation, holdout sets, or online evaluation.
- This encompasses the complete cycle of training, fine-tuning, and validating language models.
- You will be designing and adapting LLMs for use in virtual assistants, Information retrieval and extraction etc.
- Experimentation with Emerging Technologies and Methods :
- Actively exploring new technologies and methodologies in language model development, including experimental frameworks and software tools.
LLM Alignment :
- Understanding of algorithms like DPO, PPO, KPO, RLHF and using it for guardrails.
AI Data Retrieval :
- Data retrieval from unstructured data, extract key value pairs using techniques like donut, layoutLM, table transformers.
- Analyze data and build EDAs to identify data patterns
- Hands-on and strong understanding of concepts in Deep Learning and NLP Proficient in TensorFlow and similar libraries.
Required Qualifications :
- 5+ years of hands-on experience in developing and deploying Large Language Models, and Machine learning and working with Pytorch.
- A thorough understanding of machine learning, particularly deep learning techniques, including knowledge of neural network architectures, training methods, and optimization algorithms.
- Proficiency in AI technology, Python, including experience with NLP libraries (e.g., Hugging Face Transformers, NLTK, spaCy), text classification.
- Experience with frameworks: PyTorch, or Tensorflow.
- Experience with cloud services (AWS, Azure) and ML deployment tool Docker
- Familiarity with model fine-tuning and optimization techniques for LLMs.
- Proven track record of innovative solutions in the field of LLMs.
- Strong communication skills, with the ability to explain complex AI concepts to non-expert audiences.
Additional good to have qualifications :
- 4+ years' experience in data analytics, data science, quantitative analysis using statistical computer languages to draw insights from large data sets 3+ years' experience in Python development, preferably delivering production code for data applications.
- Experience with unstructured data or computer vision models is a plus.
- Experience with SQL is a big plus Extensive model implementation experience using Scikit.
- Experience designing and developing for security critical applications; experience with the specifics for HIPAA/PHI/PII/GDPR a big plus.
- Basic experience with Linux, Git, Jupyter Notebooks is must Knowledge of Agile development practices Flexibility and adaptability to respond to a rapidly changing environment.
- Experience with distributed computational techniques and job orchestration tools and platforms is very valuable: airflow, etc.
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