Key skills
PythonSQLAILarge Language ModelsOpenAIRAGData EngineeringSnowflakeAzureKubernetesDockerSQL ServerGitCI/CD
About this role
Fractal is a strategic AI partner to Fortune 500 companies aiming to enhance human decision-making through AI. The LLMOps Engineer will design, develop, and deploy AI solutions, fine-tune Large Language Models, and collaborate with cross-functional teams to integrate these solutions with existing infrastructure.
Responsibilities:
- Design, develop, and deploy AI solutions leveraging cutting-edge tools, frameworks, and best practices
- Fine-tune and deploy Large Language Models (LLMs) such as OpenAI and Azure-based services to deliver scalable AI applications
- Implement and optimize Retrieval-Augmented Generation (RAG) architectures, leveraging vector databases for efficient data retrieval
- Write clean, maintainable Python code following software development best practices, including GIT workflows, code reviews, and CI/CD pipelines
- Collaborate with cross-functional teams to integrate AI solutions with existing data engineering pipelines and cloud infrastructure
- Build and manage containerized AI applications using Docker and Kubernetes for scalability and reproducibility
- Leverage Azure services for deploying, monitoring, and scaling AI applications in the cloud
Requirements:
- Strong proficiency in Python programming with a deep understanding of the software development lifecycle
- Expertise in working with LLMs, including fine-tuning, prompt engineering, and deployment
- Hands-on experience with RAG architectures and vector databases for knowledge retrieval
- Familiarity with SQL Server and/or Snowflake for data storage and retrieval
- Proficiency in Docker for containerization and Kubernetes for orchestration of containerized applications
- Solid understanding of Azure services for deploying and managing AI applications
- Experience with advanced optimization techniques for LLMs in production
- Exposure to managing and deploying large-scale AI systems in cloud environments
- Knowledge of best practices in logging, monitoring, and debugging AI workflows
- Ability to collaborate effectively with data engineering teams for seamless integration of AI pipelines