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.