Generative AI/LLM Engineer

You will be working full-time at Soothsayer Analytics in Hyderabad as a Generative AI/LLM Engineer. Your primary responsibility will be to design, develop, and deploy generative AI models using cutting-edge technologies like Azure OpenAI GPT-4, GPT-4 Vision, or GPT-4 Omni. You should have a strong background in building and deploying AI models, with a focus on leveraging technologies such as Retrieval-Augmented Generation (RAG) and working with Vector Databases. While experience in fine-tuning large language models (LLMs) is beneficial, it is not mandatory. You are expected to have a general understanding of training or fine-tuning deep learning models and be able to quickly learn and implement advanced techniques. Your key responsibilities will include designing, developing, and deploying generative AI models using GPT-4 variants, implementing and optimizing RAG techniques, building and managing AI services using Python frameworks like LangChain or LlamaIndex, and developing APIs with FastAPI or Quart for efficient integration. You will focus on scalability, performance, and optimization of AI solutions across cloud environments, particularly with Azure and AWS. Working with Vector Databases is mandatory, and experience with Graph Databases is optional. You will also utilize Cosmos DB and SQL for data storage and management solutions, apply MLOps or LLMOps practices, and manage Azure Pipelines for continuous integration and deployment. Additionally, you will continuously research and adopt the latest advancements in AI technologies. To qualify for this role, you should have a Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field, with at least 1+ years of experience in Generative AI/LLM technologies and 5+ years of experience in related fields. Proficiency in Python and experience with frameworks like LangChain, LlamaIndex, FastAPI, or Quart is required. Expertise in RAG and experience with Vector Databases are mandatory. Knowledge of Cosmos DB and SQL is also essential. Experience with fine-tuning LLMs and Graph Databases is beneficial but not mandatory. You should have proven experience in MLOps, LLMOps, or DevOps, a strong understanding of CI/CD processes, automation, and pipeline management, and familiarity with containers, Docker, or Kubernetes. Experience with cloud platforms, particularly Azure or AWS, and cloud-native AI services is desirable. Strong problem-solving abilities and a proactive approach to learning new AI trends and best practices quickly are necessary for this role.,