Design and develop machine learning models for tasks such as text classification, sentiment analysis, entity recognition, summarisation, and language modelling.
Fine-tune and optimise transformer-based models (BERT, GPT, T5, etc.) for specific NLP applications.
Implement tokenisation, lemmatisation, stemming, and other preprocessing techniques to improve model performance.
Utilise open-source libraries such as Hugging Face Transformers, spaCy, and NLTK.
Data Processing & Feature Engineering:
Collect, preprocess, and clean large text datasets from various sources.
Perform feature engineering to enhance the quality and relevance of training data.
Implement text normalisation and data augmentation techniques to improve model generalisation.
Work with structured and unstructured data to create domain-specific language models.
Model Evaluation & Optimisation:
Evaluate model performance using appropriate metrics such as F1-score, precision, recall, and BLEU score.
Optimise models for latency, scalability, and deployment efficiency.
Conduct error analysis and implement improvements based on feedback loops.
Use transfer learning and fine-tuning strategies to improve NLP models.
Application Development & Integration:
Develop and integrate NLP solutions into production environments, such as chatbots, search engines, and recommendation systems.
Build RESTful APIs to expose NLP functionalities to web and mobile applications.
Collaborate with software engineers and product teams to align NLP capabilities with business requirements.
Deploy models using cloud platforms (AWS, GCP, Azure) and edge devices.
Research & Innovation:
Stay up to date with the latest advancements in NLP, machine learning, and AI.
Explore emerging NLP techniques, including large language models (LLMs) and multi-modal AI.
Experiment with unsupervised and semi-supervised learning approaches for text analysis.
Participate in research projects and contribute to academic publications or open-source initiatives.
Automation & Workflow Optimisation:
Implement NLP pipelines for data processing, annotation, and continuous learning.
Leverage MLOps best practices to automate training, evaluation, and deployment workflows.
Integrate NLP models with AI-powered analytics and business intelligence systems.
Security & Compliance:
Ensure NLP applications comply with data privacy and ethical AI guidelines (GDPR, HIPAA, etc.).
Implement bias detection and fairness measures within NLP models.
Ensure proper handling of sensitive and confidential data.
Qualifications:
Proven experience as an NLP Engineer or Machine Learning Engineer with a focus on natural language processing.
Strong proficiency in Python and NLP libraries such as TensorFlow, PyTorch, Hugging Face Transformers, spaCy, and NLTK.
Experience with pre-trained language models and transfer learning techniques.
Solid understanding of NLP concepts such as tokenisation, text embedding, language modelling, and sentiment analysis.
Familiarity with cloud platforms (AWS, Azure, GCP) and containerisation (Docker, Kubernetes).
Experience working with large-scale text data, databases (SQL/NoSQL), and data pipelines.
Strong problem-solving skills and the ability to work in an agile development environment.
Excellent collaboration and communication skills.
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