GenAI Engineer: LLMs, RAG & Conversational AI

Capgemini; Aguascalientes; Presencial; MXN 720.000 - 1.080.000; Jornada completa

Descripción de la vacante

Capgemini in Aguascalientes is seeking an AI/ML Engineer (QA) to design and deliver GenAI solutions. You will develop production-grade AI applications using LLMs, embeddings, and RAG, and deploy conversational workflows across platforms like Dialogflow CX.

You’ll optimize models for latency and cost while collaborating with product, engineering, and data teams to achieve reliable AI features. The role emphasizes hands-on coding in Python or Node.js, exposure to LangChain/LangGraph, and

Formación

• Strong programming in Python or Node.js.
• Experience with LLMs, RAG, embeddings and vector databases.
• Cloud deployment and MLOps fundamentals.
• Familiarity with conversational AI platforms (Dialogflow CX, GenAI playbooks).
• Exposure to LangChain and orchestration frameworks.

Responsabilidades

• Develop GenAI applications using LLMs, embeddings, and RAG architectures.
• Build and deploy chatbots, virtual assistants, and conversational workflows (Dialogflow CX, Copilot Studio).
• Implement AI workflows using LangChain, LangGraph, and vector databases.
• Optimize models for performance, latency, and cost; integrate with cloud platforms (Azure/AWS/GCP).
• Collaborate with product, engineering, and data teams to deliver reliable AI features.

Conocimientos

Python/Node.js

LLMs & embeddings

Cloud deployment & MLOps

Dialogflow CX

LangChain & LangGraph

Herramientas

Vector databases

Descripción del empleo

Capgemini in Aguascalientes is seeking an AI/ML Engineer (QA) to design and deliver GenAI solutions. You will develop production-grade AI applications using LLMs, embeddings, and RAG, and deploy conversational workflows across platforms like Dialogflow CX.

You’ll optimize models for latency and cost while collaborating with product, engineering, and data teams to achieve reliable AI features. The role emphasizes hands-on coding in Python or Node.js, exposure to LangChain/LangGraph, and