LLMOps Role

This role focuses on operationalizing and maintaining Large Language Models (LLMs) within a production environment. The ideal candidate will be responsible for ensuring the reliability, scalability, and performance of LLM-powered applications, working closely with data scientists and engineers to bridge the gap between research and deployment.

Key Responsibilities:

Develop and implement robust LLMOps pipelines for model training, evaluation, and deployment.
Monitor model performance and identify areas for improvement.
Automate model deployment and scaling processes.
Collaborate with data scientists to optimize model architectures and training strategies.
Implement and maintain infrastructure for LLM serving.
Ensure data quality and security throughout the LLM lifecycle.
Troubleshoot and resolve issues related to LLM performance and reliability.

Requirements/Qualifications:

Experience with LLM frameworks and tools (e.g., TensorFlow, PyTorch, Hugging Face).
Strong understanding of machine learning principles and techniques.
Experience with cloud platforms (e.g., AWS, Azure, GCP).
Proficiency in Python and other relevant programming languages.
Experience with DevOps practices and tools (e.g., Docker, Kubernetes).
Excellent problem-solving and communication skills.
Experience with MLOps best practices.

About the Company:

Capgemini is a global leader in technology services and consulting. They help organizations transform and achieve their business objectives through innovative solutions and services across various industries.