GenAI / AI-ML Engineers

InterSources Inc.via Indeed

RemotoUsPlenoCLTHoje

Salário Estimado

R$ 7.128,00 - R$ 10.692,00

Tecnologias

Python Go AWS IA

0de 100

Excelente

Score da Vaga

Descrição da Vaga

2) GenAI / AI-ML Engineers (2 resources) Remote 7-10 years JD:

•Engineers to design and implement RAG-based and agent-driven GenAI systems

•Work on LLM orchestration, intent resolution, and evaluation frameworks Scope of Work:

•Build RAG pipelines (retrieval + generation)

•Develop multi-agent systems and orchestration logic • Implement intent classification and entity extraction

•Optimize LLM usage for cost and performance • Design and execute evaluation frameworks (accuracy, benchmarking) Must Have:

•Experience with AWS Bedrock (Knowledge Bases, embeddings, Claude LLMs) • Hands-on with agent frameworks (AgentCore, LangChain, LangGraph, or similar)

•Strong understanding of RAG architecture (chunking, embeddings, hybrid search) • Prompt engineering (system prompts, few-shot, CoT, intent prompts)

•Strong Python skills • Experience with multi-agent orchestration patterns

•Knowledge of LLM cost optimization techniques • Understanding of NLP concepts (intent classification, entity resolution)

•Experience with evaluation frameworks (e.g., RAGAS or similar) Good to Have:

•Experience with tool integration in agents

•Knowledge of LLM observability and monitoring • Prior experience building production-grade GenAI systems

Requisitos

Work on LLM orchestration, intent resolution, and evaluation frameworks
Experience with AWS Bedrock (Knowledge Bases, embeddings, Claude LLMs)
Hands-on with agent frameworks (AgentCore, LangChain, LangGraph, or similar)
Strong understanding of RAG architecture (chunking, embeddings, hybrid search)
Prompt engineering (system prompts, few-shot, CoT, intent prompts)
Strong Python skills
Experience with multi-agent orchestration patterns
Knowledge of LLM cost optimization techniques
Understanding of NLP concepts (intent classification, entity resolution)
Experience with evaluation frameworks (e.g., RAGAS or similar)
Experience with tool integration in agents
Knowledge of LLM observability and monitoring
Prior experience building production-grade GenAI systems

Responsabilidades

Engineers to design and implement RAG-based and agent-driven GenAI systems
Build RAG pipelines (retrieval + generation)
Develop multi-agent systems and orchestration logic
Implement intent classification and entity extraction
Optimize LLM usage for cost and performance
Design and execute evaluation frameworks (accuracy, benchmarking)

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Informações

NívelPleno

ContratoCLT

LocalUs

RemotoSim

MoedaBRL

PublicadaHoje

FonteIndeed

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