Job
Description
As a Generative AI/LLM Engineer at Soothsayer Analytics, your role will involve designing, developing, and deploying AI models using cutting-edge technologies like Azure OpenAI GPT-4 variants. Your focus will be on leveraging state-of-the-art tools such as GPT-4 Vision, GPT-4 Turbo, and Retrieval-Augmented Generation (RAG) techniques to create data-driven solutions tailored to specific business needs. Key Responsibilities: - Design, develop, and deploy generative AI models using GPT-4 variants like GPT-4 Vision and GPT-4 Turbo to address specific business requirements. - Implement and optimize RAG techniques for enhanced data-driven solutions. - Build and manage AI services using Python frameworks such as LangChain or LlamaIndex, and develop APIs with FastAPI or Quart for efficient integration. - Ensure scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. - Work with Vector Databases (mandatory) and optionally Graph Databases for improved data management. - Utilize Cosmos DB and SQL for robust data storage and management solutions. - Apply MLOps or LLMOps practices to automate and streamline the AI model lifecycle, including CI/CD pipelines, monitoring, and maintenance. - Implement and manage Azure Pipelines for continuous integration and deployment. - Stay updated with the latest advancements in AI, and quickly learn and implement emerging technologies. Qualifications Required: - Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field. - Minimum 1+ years of experience in Generative AI/LLM technologies and 5+ years in related fields. - Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart. - Expertise in Retrieval-Augmented Generation (RAG) and experience with Vector Databases (mandatory). - Experience with Cosmos DB and SQL. - Fine-tuning LLMs and experience with Graph Databases are good to have but not mandatory. - Proven experience in MLOps, LLMOps, or DevOps with a strong understanding of CI/CD processes, automation, and pipeline management. - Familiarity with containers, Docker, or Kubernetes is a plus. - Familiarity with cloud platforms, particularly Azure or AWS, and experience with cloud-native AI services. - Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly. As a Generative AI/LLM Engineer at Soothsayer Analytics, your role will involve designing, developing, and deploying AI models using cutting-edge technologies like Azure OpenAI GPT-4 variants. Your focus will be on leveraging state-of-the-art tools such as GPT-4 Vision, GPT-4 Turbo, and Retrieval-Augmented Generation (RAG) techniques to create data-driven solutions tailored to specific business needs. Key Responsibilities: - Design, develop, and deploy generative AI models using GPT-4 variants like GPT-4 Vision and GPT-4 Turbo to address specific business requirements. - Implement and optimize RAG techniques for enhanced data-driven solutions. - Build and manage AI services using Python frameworks such as LangChain or LlamaIndex, and develop APIs with FastAPI or Quart for efficient integration. - Ensure scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. - Work with Vector Databases (mandatory) and optionally Graph Databases for improved data management. - Utilize Cosmos DB and SQL for robust data storage and management solutions. - Apply MLOps or LLMOps practices to automate and streamline the AI model lifecycle, including CI/CD pipelines, monitoring, and maintenance. - Implement and manage Azure Pipelines for continuous integration and deployment. - Stay updated with the latest advancements in AI, and quickly learn and implement emerging technologies. Qualifications Required: - Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field. - Minimum 1+ years of experience in Generative AI/LLM technologies and 5+ years in related fields. - Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart. - Expertise in Retrieval-Augmented Generation (RAG) and experience with Vector Databases (mandatory). - Experience with Cosmos DB and SQL. - Fine-tuning LLMs and experience with Graph Databases are good to have but not mandatory. - Proven experience in MLOps, LLMOps, or DevOps with a strong understanding of CI/CD processes, automation, and pipeline management. - Familiarity with containers, Docker, or Kubernetes is a plus. - Familiarity with cloud platforms, particularly Azure or AWS, and experience with cloud-native AI services. - Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly.
