ML Engineer (LLM)

About the company

A tech company in the sports betting and gaming analytics space is expanding its internal AI team and is looking for an ML Engineer with experience in Large Language Models.

You will be working on LLM-powered solutions to enhance various aspects of the business, including customer support automation, content generation, and decision support systems. The role involves fine-tuning, optimizing, and deploying LLMs for real-world applications in the sports betting industry.

What you'll be doing: 

• Develop and fine-tune LLMs for a range of applications within the company.
• Optimize model inference to ensure quick and efficient response times in production.
• Build and enhance retrieval-augmented generation (RAG) pipelines.
• Create and manage evaluation workflows to measure model performance and consistency.
• Deploy models in scalable, production-ready environments, ensuring stability and robustness.
• Collaborate with software engineers to integrate LLM-based APIs into company products.
• Keep up with the latest advancements in research and implement best practices in LLM development.

Requirements:

• Experience fine-tuning and deploying LLMs in real-world applications.
• Proficiency in Python and hands-on experience with Hugging Face Transformers and either PyTorch or TensorFlow.
• Experience with major LLM APIs (OpenAI, Anthropic, Claude, Gemini, etc.) and/or open-source models (LLaMA, Mistral, Falcon).
• Solid understanding of LLM architecture, prompt engineering, and text generation evaluation methods.
• Basic knowledge or practical experience with retrieval-augmented generation (RAG) and LLM-based search.
• Familiarity with model optimization techniques (quantization, distillation) is a plus.
• Strong coding skills with attention to clean code, testing, and CI/CD practices.
• Experience with Docker and Kubernetes; cloud platforms like AWS, GCP, or Azure is a plus.
• Proactive mindset with a willingness to take ownership and grow in the LLM domain.

What can we offer: 

• Employment under an employment or service contract, depending on the candidate's location.
• Remote or hybrid work format.
• Flexible working hours and additional vacation days.
• Open and supportive corporate culture with a friendly atmosphere.
• Equipment provided for work.
• Development and training opportunities: language course reimbursement, access to educational programs.