LLM Engineer Singapore Non-Remote

1 week ago

Singapore BetterData Pte Ltd Full time

We are seeking a proactive Large Language Model (LLM) Engineer to lead the development of text generation systems. The ideal candidate will possess a deep understanding of deep learning and advanced NLP techniques. Key Responsibilities: Architect and fine-tune large language models to generate synthetic text grounded to the statistics of a set of tabular data. Collaborate with cross-functional teams, including AI researchers and engineers, to integrate LLM solutions into existing synthetic data generation pipelines. Stay updated on the latest advancements in LLMs, ensuring our solutions remain at the forefront of technology. Implement prompt engineering strategies to enhance the quality and relevance of generated text. Be familiar with finetuning open weights model like Qwen-2, Llama-3, Mistral for Instruction finetuning and DPO. Be familiar with training frameworks like TRL, Unsloth, Hugging face ecosystem etc. Evaluate and optimize model performance. Develop and maintain comprehensive documentation for model architectures, training recipes and deployment procedures. Essential Skills and Qualifications: High Priority: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Proven experience in developing and deploying large language models, with a focus on text generation. Strong understanding of LLM and deep learning techniques, such as Prompt Engineering, Fine-Tuning, RAG, LoRA, etc. Strong proficiency in programming languages including Python and deep learning frameworks like PyTorch and Tensorflow. Familiarity with prompt engineering techniques and their application in enhancing LLM outputs. Excellent problem-solving skills and the ability to work collaboratively in a fast-paced startup environment. Strong communication skills, with the ability to articulate complex technical concepts to non-technical stakeholders. Quick learner, able to quickly learn and follow new advancements in industry and academia and evaluate and

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