LLM Engineer: Build, Fine-Tune & Deploy AI Solutions

KDCI; Job; Hybride; USD 120 000 - 190 000; Plein temps

Résumé du poste

KDCI Outsourcing is seeking an LLM Engineer to design, fine-tune, and deploy large language model solutions that address real business problems. You will work across the model lifecycle—from prompt and architecture design to evaluation and production monitoring—collaborating with product and engineering teams to ship reliable AI features.

In this role you will stay current with advances in transformer architectures and ML tooling, implement RAG strategies, and ensure models perform safely in

Qualifications

• 3+ years of hands-on experience with large language models and NLP.
• Strong understanding of transformer architectures (GPT, BERT, LLaMA, etc.).
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Experience with fine-tuning, prompt engineering, and hyperparameter optimization.
• Track record of deploying ML/LLM systems in production environments.

Responsabilités

• Design, fine-tune, and optimize large language models for specific use cases
• Build and maintain evaluation harnesses to measure model quality and safety
• Implement RAG pipelines and prompt strategies
• Monitor model performance in production and iterate on improvements
• Collaborate with product and engineering teams to integrate LLM features
• Stay current with emerging LLM research, architectures, and tooling

Connaissances

LLM / NLP

Transformer architectures

Python

Fine-tuning / prompt engineering

Production ML deployment

RAG pipelines

Outils

PyTorch

TensorFlow

Description du poste

KDCI Outsourcing is seeking an LLM Engineer to design, fine-tune, and deploy large language model solutions that address real business problems. You will work across the model lifecycle—from prompt and architecture design to evaluation and production monitoring—collaborating with product and engineering teams to ship reliable AI features.

In this role you will stay current with advances in transformer architectures and ML tooling, implement RAG strategies, and ensure models perform safely in