Prompt Engineer Chalhoub Group

What you'll be doing

• Design and optimize prompts to improve LLM outputs across diverse use cases.
• Experiment with prompt techniques (few-shot, chain-of-thought, tool/function calling, RAG,...) to maximize relevance, accuracy, and safety.
• Develop and maintain prompt libraries and templates, ensuring reusability, consistency, and alignment with brand and business needs.
• Benchmark and evaluate model responses using defined success metrics (accuracy, coherence, hallucination rate, tone compliance).
• Integrate prompts into production workflows, working closely with MLOps and engineering teams to ensure scalability and stability.
• Monitor prompt performance post-deployment, iterating based on feedback, drift, and new business requirements.
• Ensure responsible use of AI outputs, embedding bias checks, safety guardrails, and compliance with internal AI governance.
• Stay up to date with GenAI advancements and proactively introduce innovative prompting techniques.
• Contribute to AI literacy and adoption, documenting prompt guidelines and training super-users or functional teams on best practices.

What you ll need to succeed

• 2 5 years of experience in data science, software engineering, NLP, computational linguistics, or related applied AI roles.
• Strong proficiency in Python and common AI/LLM libraries (Hugging Face, LangChain, OpenAI SDKs, ...).
• Practical experience designing and testing prompts for LLMs (chatbots, copilots, content generation, or workflow automation).
• Understanding of LLM architectures, embeddings, RAG pipelines, and fine-tuning approaches at a working literacy level.
• Familiarity with evaluation methods for LLM outputs, including accuracy, hallucination reduction, bias detection, and A/B testing.
• Hands-on experience with API integration and connecting LLMs into enterprise workflows or apps.
• Exposure to MLOps concepts (deployment, monitoring, drift detection,...), with ability to collaborate effectively with engineering teams.
• Awareness of AI governance and responsible AI practices, including fairness, transparency, and brand alignment.
• Strong analytical and problem-solving skills, with the ability to translate business needs into effective AI prompts.
• Excellent communication and collaboration skills, working cross-functionally with product, business, and technical stakeholders.