Big Data Engineer

JOB RESPONSIBILITIES

• Conduct research and experimentation on state-of-the-art LLM architectures, data curation techniques, and training recipes.
• Develop and implement novel approaches across the model lifecycle: pre-training, post-training (preference optimization, RLHF), prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and memory engineering.
• Apply cutting-edge NLP advancements to improve Khmer text understanding, including tokenization, word segmentation, and interpretability of model behavior on Khmer.
• Identify, collect, and curate high-quality, diverse datasets in Khmer for LLM training, validation, and testing.
• Build and maintain scalable data pipelines for large-scale collection, cleaning, deduplication, and filtering of Khmer text.
• Generate and curate synthetic and augmented Khmer data to address gaps in low-resource domains.
• Train and fine-tune Large Language Models using extensive Khmer datasets.
• Optimize LLM performance for tasks such as text generation, summarization, translation, question answering, and sentiment analysis in Khmer.
• Collaborate closely with MLOps Engineers to integrate trained LLMs.
• Work on containerization (Docker) and orchestration (Kubernetes) of LLM services.
• Build and maintain data visualization and analytics pipelines to monitor model performance, training metrics, and data quality using enterprise big data tools.
• Design and implement rigorous evaluation methodologies and metrics tailored for Khmer LLMs.
• Conduct comprehensive testing and analysis to identify model biases, ethical concerns, and areas for improvement.

JOB REQUIREMENTS

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, or a related field.
• Solid understanding of LLM architectures and their underlying mechanisms.
• Familiarity with MLOps principles, CI/CD pipelines, Docker, and Kubernetes.
• Understanding of Khmer linguistics and script characteristics, including word segmentation and tokenization challenges.
• Knowledge of data structures, algorithms, and software engineering best practices.
• Minimum of 3+ years of hands-on experience in Natural Language Processing (NLP) or Machine Learning engineering.
• Proven experience working with Large Language Models (LLMs), including pre-training, fine-tuning, or deployment.
• Strong proficiency in Python and relevant NLP/ML libraries
• Experience with cloud platforms for training and deploying ML models.
• Experience building and managing large-scale datasets and data pipelines.
• Strong analytical and problem-solving mindset, with attention to detail in experimentation and evaluation.
• Clear communicator, able to explain complex technical concepts to both technical and non-technical audiences.
• High integrity in handling data, respecting privacy, licensing, and ethical considerations.
• Intellectual curiosity and self-motivation to keep pace with a fast-moving research field.
• Collaborative team player, able to work closely across teams and with stakeholders.