Job 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
Required Qualifications:
  • 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)
Benefits:
  • Delivering innovative solutions to industry leaders, making a global impact
  • Enjoyable working environment, whether it is the vibrant office or the comfort of your home
  • Opportunity to work abroad for up to two months per year
  • Relocation opportunities within our offices in 55+ countries
  • Corporate and social events
  • Leadership development, career advising, soft skills and well-being programs
  • Certifications, including GCP, Azure and AWS
  • Unlimited access to EPAM's internal learning database
  • Free English classes with certified teachers
  • Participation in the Employee Stock Purchase Plan
  • Monetary bonuses for engaging in the referral program
  • Comprehensive medical & family care package
  • Four trust days per year for personal needs
  • Discounts for fitness clubs
  • Benefits package (hotels, restaurants, stores and services)
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
Skills:
  • AI Solution Engineering
  • Large Language Models (LLM)
  • Hugging Face Transformers
  • Low-rank adaptation (LoRA)
  • LLM evaluation
  • LLMOps
  • Google Vertex AI
  • Reinforcement Learning from Human Feedback (RLHF)
  • Distillation for Deep Learning Models
  • Quantization for Deep Learning Models
  • vLLM
  • LLM security
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