Description
We are seeking a Generative AI Engineer to own how the platform's language models are improved — from fine-tuning and evaluation to deployment. You will partner with Back-End engineers who handle the serving infrastructure, and collaborate with Data Analysts and business stakeholders who define what a "good" recommendation actually looks like.
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
Requirements
•5+ years of hands-on experience with Large Language Models (LLM) fine-tuning — SFT and PEFT/LoRA at minimum, ideally preference optimization (DPO or similar)
•At least 1 year of relevant leadership experience
•Proficiency in Hugging Face Transformers/PEFT/TRL, Axolotl, or Unsloth, or managed tuning on Vertex AI / Bedrock / OpenAI
•Understanding of training mechanics, including tokenization, context length and packing, and learning-rate and hyperparameter choices
•Knowledge of catastrophic forgetting, overfitting on small domain sets, and GPU/TPU memory constraints
•Expertise in LLM evaluation — designing eval sets and metrics for generative tasks, with healthy skepticism about benchmark scores that don't reflect user outcomes
•Skills in data curation with strong instinct for dataset quality over quantity — labeling guidelines, inter-annotator agreement, and leakage control
•Familiarity with synthetic data pitfalls and filtering strategies
•Background in LLMOps, including model and prompt versioning, drift monitoring, and retraining lifecycle management
•Competency in Google Cloud Platform (Vertex AI, GCS, Cloud Run) and Git-based workflows
•Sufficient Python skills to work in notebooks, training scripts, and eval pipelines, and to adapt existing framework code
•Excellent command of written and spoken English (B2+ level)
Nice to have
•Experience with Reinforcement Learning from Human Feedback (RLHF) and preference-based alignment methods
•Familiarity with distillation and quantization techniques (GPTQ, AWQ, GGUF, bitsandbytes) and their quality cost
•Understanding of inference stacks such as vLLM, TGI, and Vertex AI endpoints, including streaming and latency/throughput implications
•Knowledge of LLM security, including safety guardrails, scope controls, and PII handling for enterprise commercial data
Conditions
No conditions stated in the posting.
