AI Model Developer- LLM,RAG - [REMOTE]

Remote Full-time
Job Title: AI Model Developer Location: [Remote] Job Summary: We are looking for a skilled and innovative AI Model Developer to join our team. The ideal candidate will have hands-on experience with Large Language Models (LLMs), particularly OpenAI and Azure OpenAI, to develop advanced AI solutions. This role involves building, fine-tuning, and deploying AI models that improve performance and user experience. Key Responsibilities: β€’ Develop and implement AI models using OpenAI's ChatGPT API or similar LLMs, with a focus on prompt engineering and fine-tuning. β€’ Leverage retrieval-augmented generation (RAG) techniques for enhanced performance and retrieval capabilities. β€’ Write Python scripts for data processing, model deployment, and API integration. β€’ Integrate AI solutions with RESTful APIs and third-party services (e.g., OpenAI). β€’ Manage structured and unstructured data using SQL and MongoDB. β€’ Use Azure Databricks for data processing and model training. β€’ Implement embedding models for efficient vector storage and retrieval. β€’ Work with distributed data systems such as Databricks and Apache Spark. β€’ Troubleshoot AI model issues and debug system integrations. β€’ Conduct clustering, embedding creation, and evaluation for various data types. β€’ Apply model alignment and bias mitigation strategies. β€’ Document processes, changes, and system designs for traceability. β€’ Evaluate embeddings, retrievers, and generation models using tools like LangSmith and MLFlow. Required Qualifications: β€’ Experience with OpenAI's ChatGPT API or similar LLMs, including Azure OpenAI for generation and embeddings. β€’ Familiarity with fine-tuning LLMs and prompt engineering, especially within Azure OpenAI. β€’ Proficiency in Python for scripting, data processing, and integration tasks. β€’ Experience working with APIs (OpenAI, RESTful APIs). β€’ Strong SQL skills and experience with MongoDB, including vector storage/indexing. β€’ Experience with vector databases such as Pinecone and FAISS. β€’ Knowledge of databases like Redis, SQL Server, and MongoDB. β€’ Experience with embedding models for vector data retrieval. β€’ Solid debugging and integration troubleshooting skills. β€’ Strong problem-solving and analytical thinking. β€’ Excellent documentation practices for tracking changes and system designs. β€’ Familiarity with LangChain and LangGraph frameworks. β€’ Hands-on experience with Pinecone for vector storage and retrieval. Apply tot his job Apply tot his job
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