Generative AI Engineer (LLM)

Responsibilities

• Run supervised fine-tuning and parameter-efficient adaptation (LoRA/QLoRA, adapters) and preference-based alignment (DPO, ORPO, RLHF-style methods) to shape tone, output structure, and domain reasoning for retail next-best-action and content-compliance tasks
• Own the dataset lifecycle — sourcing and curating examples from platform data and expert feedback, designing annotation and review workflows, deduplication, and decontamination against eval sets
• Generate and filter synthetic data, and version every dataset used in training
• Reduce cost and latency through distillation into smaller models, quantization, batching and caching strategy, and model routing — while holding quality above an agreed threshold
• Package and deploy tuned models on Vertex AI (or equivalent), and manage model and prompt versioning
• Monitor drift and quality decay in production, and define the retraining trigger and rollback path
• Implement and tune safety, scope, and PII controls appropriate to enterprise commercial data, including refusal behavior and output validation
• Partner with commercial teams to convert qualitative expectations ("this recommendation reads wrong") into training signal and measurable eval criteria

Languages