Data Scientists With Databricks, Genai & Llm

As a Data Scientist with experience in Databricks, GenAI, and LLM, your role will involve working on the GenAI/LLM layer with a core focus on Databricks expertise. Specifically, you will be responsible for: - Utilizing strong Databricks expertise in Generative AI. - Collaborating within the Databricks GenAI ecosystem. - Building and optimizing RAG/search capabilities. - Integrating data with supervisor agents. - Fine-tuning LLM behaviors. In this role, you must possess the following mandatory skills: 1. Databricks Experience: - Strong hands-on experience with Databricks, particularly in ML/LLMOps workflows. - Familiarity with Mosaic AI, Unity Catalog, Vector Search/Vector Indexing, Genie Agents, and GenAI application development on Databricks. 2. GenAI/LLM Experience: - Proficiency in prompt engineering, RAG architectures, LLM optimization, and fine-tuning concepts. - Experience with vector databases/indexing, search and retrieval systems, accuracy evaluation of LLMs, and Agentic AI concepts. 3. AI/ML Engineering Mindset: - Expertise as AI/ML Engineers, Applied GenAI Engineers, and LLM Engineers. Additionally, while the environment is primarily AWS-based, experience with Azure and Databricks is also acceptable. As a Data Scientist with experience in Databricks, GenAI, and LLM, your role will involve working on the GenAI/LLM layer with a core focus on Databricks expertise. Specifically, you will be responsible for: - Utilizing strong Databricks expertise in Generative AI. - Collaborating within the Databricks GenAI ecosystem. - Building and optimizing RAG/search capabilities. - Integrating data with supervisor agents. - Fine-tuning LLM behaviors. In this role, you must possess the following mandatory skills: 1. Databricks Experience: - Strong hands-on experience with Databricks, particularly in ML/LLMOps workflows. - Familiarity with Mosaic AI, Unity Catalog, Vector Search/Vector Indexing, Genie Agents, and GenAI application development on Databricks. 2. GenAI/LLM Experience: - Proficiency in prompt engineering, RAG architectures, LLM optimization, and fine-tuning concepts. - Experience with vector databases/indexing, search and retrieval systems, accuracy evaluation of LLMs, and Agentic AI concepts. 3. AI/ML Engineering Mindset: - Expertise as AI/ML Engineers, Applied GenAI Engineers, and LLM Engineers. Additionally, while the environment is primarily AWS-based, experience with Azure and Databricks is also acceptable.