LLM Post-Training Engineer

使用ツール

募集内容

• Design and execute supervised fine-tuning (SFT) pipelines that inject domain-specific knowledge into base LLMs across Customer Support and Network Deployment contexts
• Apply parameter-efficient fine-tuning techniques — including LoRA, QLoRA, and adapter-based methods — to adapt large models effectively within compute and latency constraints
• Collaborate with the AI & Domain Specialist and AI Data & Integration Engineer to ensure fine-tuning datasets are high quality, domain-representative, and correctly formatted for training objectives
• Maintain a structured catalog of fine-tuned model variants, adapter checkpoints, and associated training configurations to support reproducibility and lifecycle governance
• Experience building automated data curation and synthetic data generation pipelines for LLM training at scale
• Design and implement policy-reward training pipelines using advanced optimization methods including GRPO (Group Relative Policy Optimization) and SAPO (Soft Adaptive Policy Optimization)
• Build and maintain automated pipelines for domain dataset curation — sourcing, filtering, deduplicating, and formatting data at scale for both SFT and reward training
• Design synthetic data generation strategies that augment real-world datasets — producing diverse, high-quality training examples for scenarios where real data is scarce, sensitive, or imbalanced
• Collaborate with domain experts to validate synthetic data quality and ensure generated examples accurately reflect real-world telco operational scenarios
• Implement data quality gates and automated checks that maintain dataset integrity throughout the curation and generation pipeline