AI Engineer (LLM/RAG)

TL;DR

AI Engineer (LLM/RAG): Designing and developing AI models and LLMs to solve business challenges with an accent on RAG pipelines, fine-tuning via Hugging Face, and model deployment. Focus on integrating AI solutions into existing systems and optimizing model performance using large datasets.

Company

Independent technology consulting firm providing guidance and solutions to over 1000 clients globally across multiple business lines.

What you will do

• Design and develop AI models and algorithms, including LLMs, to solve business challenges.
• Implement retrieval-augmented generation (RAG) pipelines for enhanced information retrieval.
• Build, fine-tune, and deploy models using Hugging Face libraries and tools.
• Collaborate with cross-functional teams to integrate AI solutions into existing systems.
• Analyze large datasets to extract actionable insights and improve model performance.
• Monitor and maintain AI models in production environments and document technical solutions.

Requirements

• Bachelor's Degree in Artificial Intelligence, Computer Science, or a related field.
• 3+ years of experience in an AI Engineer or similar role.
• Strong knowledge of machine learning frameworks such as TensorFlow and PyTorch.
• Proficiency in LLMs, RAG techniques, and Hugging Face libraries.
• Experience with Python or R, data preprocessing, and feature engineering.
• Good communication in English.

Nice to have

• Familiarity with cloud platforms such as AWS, Azure, or GCP.

Culture & Benefits

• Competitive salary including a 13th-month salary and yearly performance appraisals.
• 14+ annual leaves and premium healthcare insurance from the probation period.
• International team environment with flexible working hours and tailor-made career paths.
• Access to technical workshops, training courses, and mobility opportunities in 60+ countries.
• Annual company trips and various teambuilding activities and sports clubs.

Hiring process

• Brief virtual or phone call to discuss motivations and fit.
• A series of interviews (average of 3) with the line manager and team members.
• Case study or technical assessment depending on the specific role.