AI/ML Specialist

Description of the TasksFollowing tasks will be performed by external service provider:- 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.Requirements:- 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.- Familiarity with leveraging graph science techniques to solve complex data problems within social networks, knowledge graphs