Engineer for Advanced RAG AI Systems -- 2
Budget: $250 – $750 USD
Experience working with AWS Bedrock, including knowledge of foundation models, embeddings, and managed knowledge base services.
Hands-on experience with frameworks used for building LLM applications and agent workflows (such as LangChain, LangGraph, or similar tools).
Strong understanding of retrieval-augmented generation concepts, including document chunking, embedding strategies, and search techniques (semantic or hybrid).
Proficiency in prompt design techniques, including structured prompts, few-shot examples, and reasoning-based prompting.
Strong programming skills in Python.
Familiarity with designing and coordinating multi-agent systems.
Understanding of strategies for optimizing LLM usage, including cost and performance trade-offs.
Knowledge of core NLP tasks such as intent detection and entity extraction.
Experience evaluating LLM systems using structured frameworks or custom metrics.
Hands-on experience with frameworks used for building LLM applications and agent workflows (such as LangChain, LangGraph, or similar tools).
Strong understanding of retrieval-augmented generation concepts, including document chunking, embedding strategies, and search techniques (semantic or hybrid).
Proficiency in prompt design techniques, including structured prompts, few-shot examples, and reasoning-based prompting.
Strong programming skills in Python.
Familiarity with designing and coordinating multi-agent systems.
Understanding of strategies for optimizing LLM usage, including cost and performance trade-offs.
Knowledge of core NLP tasks such as intent detection and entity extraction.
Experience evaluating LLM systems using structured frameworks or custom metrics.