We are seeking a Natural Language Processing (NLP) Specialist with a strong focus on Large Language Models (LLMs) to join our innovative team. In this role, you will work at the intersection of text analytics and advanced language modeling, designing and implementing solutions that leverage the power of LLMs to extract insights from unstructured textual data. You’ll collaborate with cross-functional teams to develop, optimize, and deploy language processing systems that address complex business challenges.

Key Activities:

• Develop & Implement NLP Solutions: Design, implement, and refine algorithms and models to process, understand, and generate human language from large datasets.

• Data Preparation & Feature Engineering: Prepare, clean, and structure complex textual data; engineer relevant features to optimize model performance for specific NLP tasks.

• LLM Application & Fine-tuning: Leverage, adapt, and fine-tune state-of-the-art machine learning models, with a strong emphasis on Large Language Model (LLM) applications.

• Technology Exploration & Evaluation: Utilize, test, and benchmark relevant frameworks, libraries, and tools (e.g., Hugging Face, PyTorch/TensorFlow, SpaCy) for NLP and ML tasks. Identify and experiment with novel approaches.

• Prototyping & Validation: Build prototypes, conduct evaluations, and rigorously assess the effectiveness and suitability of developed solutions.

• Documentation & Knowledge Sharing: Document methodologies, findings, and technical specifications. Present results and insights to technical and non-technical stakeholders.

• Master’s degree or higher in Computer Science, Artificial Intelligence, Computational Linguistics, Mathematics, Statistics, or a related field with strong computational and analytical capabilities.

• 2 to 5 years of professional experience in developing and implementing solutions in Natural Language Processing or Machine Learning, including hands-on experience with Large Language Models.

• Software Development: Strong expertise in Python programming (including Object-Oriented Programming best practices).

• ML/NLP Libraries: Proficiency with common Machine Learning and NLP libraries (e.g., NumPy, Pandas, Scikit-Learn, PyTorch/TensorFlow, Hugging Face Transformers, SpaCy).

• Machine Learning Foundations: Solid understanding of core Machine Learning concepts (supervised & unsupervised learning, deep learning principles).

• LLM Practical Skills: Experience implementing, training, fine-tuning, and evaluating NLP models and Large Language Models (e.g., GPT-based models, BERT, sequence-to-sequence models). Understanding of techniques like prompt engineering or RAG is beneficial.

• Data Handling: Good grasp of data processing and feature engineering techniques tailored for textual data.

• Analytical Foundations: Fundamental statistical knowledge for result interpretation and model evaluation.

• Environment: Familiarity with cloud platforms (e.g., AWS) and distributed computation (e.g. spark) is a plus.

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