AI / NLP Engineer

• 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 miningrelated 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.

What do you need to succeed in this position?

• Master + 11 years of relevant experience
• 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, plagiarismdetection, trends/anomalies detection in datasets, recommendation systems.
• Familiarity with leveraging graph science techniques to solve complex data problems within social networks, knowledge graphs.
• Proficiency in understanding and applying statistical concepts and models.
• Ability to formulate problems and develop solutions using data-driven approaches.
• Good communication skills in English, both orally and in written form.