Job Description
Job Description: Design and implement stateful multi-agent networks and/or workflows using Lang Graph and/or Lang Chain. Build and optimise end-to-end RAG pipelines, focusing on high-precision retrieval, semantic search, and the integration of diverse data sources (vector DB, graph DB, RDBMS, etc). Architect and implement multi-layered guardrails to ensure agent actions remain within business scope, enterprise safety and policy boundaries. Build and maintain high-performance AI microservices, ensuring they are optimised for OCI-compliant environments. Use of LLM evaluation frameworks to quantitatively measure agent performance.
Requirements: Expert-level proficiency in Python, specifically for asynchronous AI applications. Mastery of LangGraph and LangChain for building production-grade, stateful systems with human-in-the-loop verification patterns. Proven success in developing RAG capabilities, including experience with chunking strategies, advanced techniques like query expansion ...
Requirements: Expert-level proficiency in Python, specifically for asynchronous AI applications. Mastery of LangGraph and LangChain for building production-grade, stateful systems with human-in-the-loop verification patterns. Proven success in developing RAG capabilities, including experience with chunking strategies, advanced techniques like query expansion ...