About the Role: We are looking for an innovative LLM Developer to design, fine-tune, and deploy large language models (LLMs) that enhance retail and commercial operations. You will work on cutting-edge NLP applications-such as customer service automation, personalized recommendations, and retail analytics-leveraging state-of-the-art LLMs (e.g., GPT, Claude, Llama) to solve real-world business problems.
This role requires strong technical expertise in LLM development, NLP, and cloud deployment, along with the ability to collaborate with data analysts and commercial teams to ensure AI solutions deliver measurable business value.
Key Responsibilities:
* Develop, fine-tune, and optimize LLMs for retail-specific use cases (e.g., chatbots, sentiment analysis, product descriptions, report generation).
* Implement RAG (Retrieval-Augmented Generation) pipelines to improve LLM accuracy with domain-specific retail data.
* Collaborate with data engineers to build scalable data pipelines for preprocessing and feeding structured/unstructured data (e.g., customer reviews, product catalogs) into LLMs.
* Experiment with prompt engineering, LoRA/QLoRA fine-tuning, and RLHF to align models with business needs.
* Deploy LLM solutions on cloud platforms (e.g., AWS SageMaker, GCP Vertex AI) with a focus on scalability and cost efficiency.
* Work with cross-functional teams (data analysts, UX designers, commercial teams) to integrate LLM outputs into business workflows.
* Monitor LLM performance, address hallucination/accuracy issues, and implement guardrails for safe deployment.
* Stay updated on advancements in open-source LLMs, multimodal AI, and regulatory compliance (e.g., data privacy).
Requirements:
* Bachelor's/Master's/PhD in Computer Science, AI, NLP, or a related field.
* 2+ years of hands-on experience in LLM development, NLP, or deep learning, with a portfolio of projects (GitHub, papers, or case studies).
* Proficiency in Python and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex.
* Experience with LLM fine-tuning (e.g., LoRA, PEFT), quantization, and evaluation metrics.
* Familiarity with cloud AI services (e.g., AWS Bedrock, Azure OpenAI) and deployment tools (Docker, FastAPI).
* Knowledge of NLP techniques (tokenization, embeddings, attention mechanisms) and retail datasets (e.g., customer queries, product metadata).
* Ability to translate business requirements (e.g., "improve chatbot resolution rate") into technical solutions.
Preferred Qualifications:
* Experience with multimodal LLMs (e.g., image + text for product recommendations).
* Background in retail/e-commerce AI applications (e.g., search relevance, dynamic pricing, personalized marketing).
* Contributions to open-source LLM projects or publications in NLP conferences.
