LLM Engineer

Responsibilities:

• Design, develop, and implement advanced LLM applications using state-of-the-art models and techniques.
• Lead research initiatives in Retrieval-Augmented Generation (RAG) systems, improving the accuracy and efficiency of information retrieval and generation.
• Conduct experiments on training and fine-tuning large language models for specific domains and tasks.
• Collaborate with cross-functional teams to integrate LLM solutions into production systems.
• Research and apply modern computer vision techniques, including object detection and generative models.
• Transform unstructured data into structured formats for effective model training and analysis.
• Develop and test interfaces for mobile applications, including analysing screenshots, visual features, and debugging bugs visually.
• Stay up-to-date with the latest advancements in LLMs, NLP, computer vision, and deep learning, and apply new techniques to ongoing projects.

Requirements:

• 1+ years of experience in NLP, deep learning, and building LLM applications.
• Experience with modern computer vision techniques, such as object detection and generative models.
• Expertise in structuring unstructured data for model training and analysis.
• Hands-on experience with testing and debugging visual features in mobile app interfaces using screenshots.
• Publication record in top-tier AI conferences (e. g., NeurIPS, ICML, ACL, EMNLP) is a plus.
• Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch or TensorFlow.
• Extensive experience with transformer-based models (e. g., BERT, GPT, T5) and their applications.
• Demonstrated expertise in developing RAG systems and improving their performance.
• Proficiency in training and fine-tuning large language models, including techniques like few-shot learning and prompt engineering.
• Strong background in NLP techniques, including text preprocessing, tokenisation, and embedding methods.
• Familiarity with MLOps practices and tools for model versioning, experiment tracking, and deployment.
• Excellent problem-solving skills and ability to think creatively about AI solutions.
• Strong communication skills and ability to explain complex technical concepts to both technical and non-technical audiences.
• Experience with efficient training techniques for large models, such as distributed training and mixed-precision training.
• Knowledge of model compression techniques, including quantisation and distillation.
• Familiarity with ethical AI practices and bias mitigation in language models.
• Experience with multi-modal models combining text with other data types (e. g., images, audio).
• Contributions to open-source NLP or LLM projects.