Design, develop, and fine-tune AI models for text processing tasks such as text classification, summarization, sentiment analysis, entity recognition, and language translation.
Work with pre-trained models such as GPT, BERT, T5, and fine-tune them for domain-specific applications.
Develop custom NLP algorithms to handle language-specific tasks such as semantic analysis and syntactic parsing.
Utilise and optimise transformer-based architectures for various NLP applications.
Data Processing & Feature Engineering:
Collect, clean, and preprocess text data from various sources, ensuring high-quality datasets for model training.
Implement feature engineering techniques such as tokenisation, lemmatisation, named entity recognition (NER), and vector embeddings (Word2Vec, GloVe).
Ensure datasets align with ethical AI standards and privacy regulations.
Model Deployment & Integration:
Deploy NLP models into production environments, ensuring scalability and performance.
Build APIs and services to integrate language models into web and mobile applications.
Work with cloud platforms (AWS, Azure, Google Cloud) to deploy and maintain NLP solutions.
Implement MLOps practices for model versioning, monitoring, and retraining.
Performance Optimisation & Evaluation:
Optimise NLP models for accuracy, latency, and efficiency in production settings.
Evaluate model performance using NLP-specific metrics such as BLEU, ROUGE, perplexity, and F1-score.
Conduct A/B testing and error analysis to continuously improve language models.
Conversational AI & Chatbots:
Develop and enhance chatbot models using frameworks such as Rasa, Dialogflow, or Botpress.
Implement context-aware conversational AI systems to improve user interactions.
Integrate AI chatbots with third-party services, CRMs, and customer support systems.
Collaboration & Documentation:
Collaborate with cross-functional teams including data engineers, UX designers, and software developers to create AI-driven products.
Document model architectures, workflows, and research findings.
Provide technical insights and recommendations to improve NLP capabilities.
Research & Innovation:
Stay updated with the latest advancements in AI language models, including generative AI and large-scale pre-trained transformers.
Experiment with prompt engineering techniques to enhance model performance for specific tasks.
Explore multimodal AI solutions that combine text, speech, and visual data.
Qualifications:
Proven experience as an AI Language Developer, NLP Engineer, or similar role.
Strong programming skills in Python, with experience in NLP libraries such as spaCy, NLTK, Hugging Face Transformers, and OpenAI API.
Experience with deep learning frameworks such as TensorFlow and PyTorch.
Knowledge of statistical language modelling techniques, embeddings, and tokenisation methods.
Familiarity with cloud-based NLP services (AWS Comprehend, Google NLP, Azure Cognitive Services).
Proficiency in designing RESTful APIs for AI integrations.
Strong understanding of linguistics, grammar, and semantics across multiple languages is a plus.
Experience with prompt engineering and fine-tuning of large language models (LLMs).
Knowledge of ethical AI practices, bias mitigation, and responsible AI guidelines.
Excellent analytical and problem-solving skills with a strong attention to detail.
Global talent for startups that can’t afford to fail.
Access the top performing teams and save up to 50% on salaries whilst doing it.
