AI/ML Specialist

Description of the Tasks

Following 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