DESCRIPTION OF THE TASKS Design, implement and optimise advanced AI, NLP, and ML models. Use LLMs, RAG frameworks, and other state-of-the-art approaches. Create methods for tokenisation, part-of-speech tagging, named entity recognition, classification, clustering and other text mining-related tasks. Fine-tune pre-trained models on domain-specific tasks. Conduct thorough research and stay updated on the latest trends and advancements in NLP, ML, and AI technologies. Develop and maintain robust, scalable, and efficient code using Python. Collaborate with cross-functional teams to integrate AI/ML solutions into existing products and services. Perform rigorous analysis and experimentation to improve model accuracy, efficiency, and scalability. Participate in peer reviews and contribute to the continuous improvement of AI solutions. Contribute to the design and implementation of ML application architecture and its solution stack. Develop comprehensive reports and visualisations to communicate insights and findings to stakeholders. KNOWLEDGE AND SKILLS Experience in Machine Learning and Natural Language Processing. Excellent knowledge of Python and libraries (e.G. Pandas, SpaCy, NLTK, Hugging Face). Experience with deep learning frameworks for complex model architecture such as TensorFlow or PyTorch. Experience with AI-powered code assistants (e.G., Amazon Q, Github Copilot), staying updated with advancements in AI-driven code technologies. Good knowledge of SQL tooling (Oracle, PostgreSQL). Knowledge of NoSQL databases (Elasticsearch, MongoDB). Knowledge of architectural design of scalable ML solutions such as model servers, GPU resource optimisation. Experience with A/B testing and experimental design of ML models. Experience with pre-trained models and LLMs like GPT, and other Transformer-based architectures. Experience with tools like Matplotlib and Seaborn for creating data visualizations. Strong understanding of linguistics and text processing techniques. Proficient in continuous code delivery and unit testing. Understanding of bias in ML applications and bias mitigation techniques. Knowledge in one of the following areas: predictive (forecasting, recommendation), prescriptive (simulation), topic detection, plagiarism detection, trends/anomalies detection in datasets, recommendation systems. Proficiency in understanding and applying statistical concepts and models. Ability to formulate problems and develop solutions using data-driven approaches. Effectively communicating complex data insights to non-technical stakeholders. LEVEL OF EDUCATION Bachelor Degree