Data Scientist (GPT/BERT Expert)

We are looking for a highly skilled Data Scientist with deep expertise in implementing GPT and BERT models to work on both structured and unstructured data. The ideal candidate will have hands-on experience deploying and fine-tuning large language models (LLMs) such as GPT and BERT in cloud environments, specifically Azure and AWS. You will be responsible for developing, optimizing, and scaling machine learning models to solve complex business problems using a combination of structured datasets and unstructured text data.
Responsibilities:
Design and implement solutions using GPT, BERT, and other transformer models to process both structured (e.g., tabular) and unstructured (e.g., text, document) data.
Fine-tune pre-trained language models for specific business use cases, such as natural language processing (NLP), document classification, and entity extraction.
Build and deploy scalable machine learning pipelines in cloud environments, primarily using Azure and AWS.
Collaborate with data engineers and cloud architects to develop robust, high-performance solutions for data processing and model inference.
Apply advanced machine learning techniques, including transfer learning, to maximize model performance on diverse datasets.
Design and execute experiments to evaluate model performance and improve accuracy, efficiency, and scalability.
Integrate models into production workflows and monitor their performance through A/B testing and continuous improvement cycles.
Work closely with cross-functional teams to ensure that model outputs meet business and technical requirements.
Implement context-aware models that can handle context menus and complex NLP use cases, such as chatbots, voice assistants, and document summarization.
Stay current with the latest research and advancements in NLP, GPT/BERT models, and cloud-based machine learning.

Skills & Qualifications:
GPT/BERT Expertise: 3+ years of experience working with transformer-based models like GPT, BERT, or similar, with a focus on NLP applications.
Cloud Platforms: Strong experience with cloud-based machine learning services on Azure and AWS (e.g., SageMaker, Azure Machine Learning).
Data Processing: Proficiency in handling structured data (e.g., tabular data) and unstructured data (e.g., text, documents, JSON) for feature extraction and analysis.
ML Frameworks: Hands-on experience with TensorFlow, PyTorch, Hugging Face, or similar machine learning frameworks for building and fine-tuning transformer models.
NLP Techniques: In-depth understanding of natural language processing techniques such as tokenization, attention mechanisms, embeddings, and transfer learning.
Cloud Deployment: Experience deploying models in cloud environments (Azure, AWS) with scalable architecture, utilizing serverless or containerized solutions.
API Integration: Ability to integrate models with APIs and build context-aware models that can interact with context menus and other UI elements.
Version Control & Collaboration: Proficiency in Git and version control systems, working within Agile or DevOps teams.
Model Evaluation: Experience with model evaluation metrics such as precision, recall, F1-score, and the ability to improve models through continuous retraining and feedback loops.
Communication: Strong verbal and written communication skills to collaborate with stakeholders and present technical concepts to non-technical audiences.
Preferred Qualifications:
Experience with Azure Cognitive Services and AWS NLP services like Comprehend and Textract.
Familiarity with modern architecture patterns, including microservices and event-driven systems.
Hands-on experience with knowledge graphs, semantic search, or document intelligence solutions.
Experience with building conversational AI systems or context-aware chatbots using GPT/BERT.
Knowledge of DevOps practices, including CI/CD pipelines, for machine learning model deployment.

Education:
Master s or PhD in Computer Science, Data Science, Machine Learning, or a related field, or equivalent practical experience.