Senior Data Scientist

3 weeks ago

india Circuit Compilers Full time

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.

(ref:hirist.tech)

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