Practical Trainee

Job Descriptions:

• SLM Development & Fine-Tuning: Assist in researching, selecting and fine-tuning open-source Small Language Models (SLMs).
• Cost Optimization Research: Benchmark model performance against API costs to ensure the developed SLMs deliver high efficiency and accuracy at a significantly lower operational cost.
• Data Engineering & Curation: Collect, clean and structure domain-specific real estate and business datasets required to train and optimize the specialized SLMs.
• Model Evaluation & Testing: Quantize, compress and test models to ensure they can run efficiently on smaller infrastructure without losing critical conversational or analytical capabilities.
• AI Agent Integration: Collaborate with ICT team to deploy and integrate these custom SLMs into PG’s existing AI agent workflows and customer-facing platforms.
• Documentation & Knowledge Sharing: Document the training methodologies, dataset parameters and model performance metrics to help build PG's internal AI knowledge base.

Job Requirements:

• Currently pursuing an undergraduate degree in Data Science, Computer Science (AI/Software Engineering) or a related quantitative field.
• Strong foundational knowledge in Python and familiarity with libraries like PyTorch, TensorFlow, or Hugging Face Transformers.
• AI Concepts: Good understanding of Natural Language Processing (NLP), Large Language Models (LLMs) and data preprocessing techniques.
• Eager to learn, innovate and grow with the company, with a strong interest in solving real-world cost and engineering challenges.