Build an AI-powered intent generation tool that extracts intents and answers from PRDs, and generates sample user queries to help the CS team build RAG knowledge bases more efficiently.
Develop an intent similarity detection pipeline using semantic search to identify duplicate or highly similar intents, and integrated it into the intent generation workflow for knowledge base quality control.
Enhance the intent management platform by implementing review and approval workflows, production gating, distributed task persistence, and multi-worker task recovery.
Researche Voice AI for potential integration into the existing chatbot, evaluating speech recognition, text-to-speech, and system architecture.
Key Skills Required
Agentic Frameworks: Hands-on experience with frameworks like LangChain, LangGraph, or, CrewAI
LLM Architectures & Tool Use: Expertise in prompt engineering, fine-tuning, function calling/API integration, and evaluation frameworks for LLM-driven agents.
Programming & Systems: Advanced Python skills; familiarity with microservices, Docker, REST APIs, and event-driven architectures.
Vector Databases & Storage: Proficiency with vector stores (e.g., Pinecone, Qdrant, Chroma, Milvus) for memory persistence and context retrieval.
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Tasks
Build an AI-powered intent generation tool that extracts intents and answers from PRDs, and generates sample user queries to help the CS team build RAG knowledge bases more efficiently.
Develop an intent similarity detection pipeline using semantic search to identify duplicate or highly similar intents, and integrated it into the intent generation workflow for knowledge base quality control.
Enhance the intent management platform by implementing review and approval workflows, production gating, distributed task persistence, and multi-worker task recovery.
Researche Voice AI for potential integration into the existing chatbot, evaluating speech recognition, text-to-speech, and system architecture.
Key Skills Required
Agentic Frameworks: Hands-on experience with frameworks like LangChain, LangGraph, or, CrewAI
LLM Architectures & Tool Use: Expertise in prompt engineering, fine-tuning, function calling/API integration, and evaluation frameworks for LLM-driven agents.
Programming & Systems: Advanced Python skills; familiarity with microservices, Docker, REST APIs, and event-driven architectures.
Vector Databases & Storage: Proficiency with vector stores (e.g., Pinecone, Qdrant, Chroma, Milvus) for memory persistence and context retrieval.
experience
4 years
skills
Python, SQL, Langchain, LangGraph
qualifications
Bachelors in Computer Science, Data Science, Artificial Intelligence, or Information Technology
education
Bachelor Degree
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