Dimiour - Generative AI Engineer - Large Language Models

Responsibilities

• Design, develop, and implement Generative AI solutions, with a core focus on Large Language Models (LLMs).
• Build and manage the infrastructure required for training, fine-tuning, and deploying LLMs in a scalable manner.
• Utilize Python and relevant AI/GenAI frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers) for model development and experimentation.
• Employ data manipulation libraries such as Pandas and NumPy for efficient data preprocessing and analysis crucial for GenAI models.
• Develop robust and scalable APIs using FastAPI to expose GenAI models and functionalities to other applications and services.
• Apply strong software engineering principles, including version control (Git), continuous integration and continuous deployment (CI/CD), and automated testing, throughout the GenAI solution development lifecycle.
• Implement and manage MLOps workflows for the efficient deployment, monitoring, and maintenance of GenAI models in a production environment.
• Utilize DevOps tools such as Docker, Kubernetes, Jenkins, and Terraform to automate infrastructure provisioning and application deployment for AI/GenAI solutions.
• Implement and maintain advanced CI/CD pipelines to ensure the rapid and reliable delivery of GenAI applications.
• Collaborate effectively with data scientists, researchers, and other engineers in a remote setting to translate business requirements into technical GenAI solutions.
• Contribute to the design and architecture of our remote AI platform and infrastructure.
• Stay up-to-date with the latest advancements in Generative AI, LLMs, MLOps, and related technologies.
  

Skills Required

• 5+ years of professional experience with a strong focus on Python development and AI/GenAI frameworks.
• Proven expertise in implementing and managing Large Language Models (LLMs).
• Strong proficiency in Python and experience with AI/GenAI frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
• Extensive experience with data manipulation libraries such as Pandas and NumPy.
• Solid experience in developing APIs using FastAPI.
  

Comprehensive Understanding Of Software Engineering Principles, Including

• Version control using Git.
• Continuous Integration and Continuous Deployment (CI/CD) practices.
• Automated testing methodologies.
• Essential experience in MLOps and ML Engineering, with a demonstrable track record of developing and maintaining AI solutions in production.
• Proficiency in DevOps tools such as Docker, Kubernetes, Jenkins, and Terraform.
• Experience implementing advanced CI/CD pipelines.
• Excellent written and verbal communication skills for effective remote collaboration.
• Ability to work independently and proactively in a remote environment.