AI/ML Engineer, QA

Capgemini

. Build, integrate, and optimize AI solutions using LLMs, RAG pipelines, conversational AI platforms, and modern AI orchestration frameworks .

Posted 9/16/2026full-timeAguascalientes • MexicoMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and optimizing AI solutions using LLMs, RAG architectures, and cloud platforms. Proficient in deploying conversational AI applications and implementing machine learning workflows to enhance performance and reliability.

Highest-signal resume keywords

Python ProgrammingLLMs and RAG ArchitecturesCloud Deployment (Azure, AWS, GCP)Conversational AI Platforms (Dialogflow CX)MLOps Fundamentals

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine Learning TechniquesModel OptimizationEmbedding TechniquesVector DatabasesAI Workflow Implementation

Soft Skills

CollaborationProblem Solving

Tools & Technologies

LangChainLangGraphDialogflow CXCopilot Studio

Industry Keywords

Conversational AIGenAI ApplicationsAI Orchestration FrameworksMultimodal ModelsAgentic AI

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformJavaScriptNode.jsPython

About the role

Key responsibilities & impact

Build, integrate, and optimize AI solutions using LLMs, RAG pipelines, conversational AI platforms, and modern AI orchestration frameworks
Develop GenAI applications using LLMs, embeddings, and RAG architectures
Build and deploy chatbots, virtual assistants, and conversational workflows using Dialogflow CX and Copilot Studio
Implement AI workflows using LangChain, LangGraph, and vector databases
Optimize models for performance, latency, and cost
Integrate AI solutions with Azure, AWS, and GCP
Collaborate with product, engineering, and data teams to deliver reliable AI features
Apply machine learning techniques and algorithms to solve complex problems, analyze data, and develop intelligent systems
Design solutions and support implementation of machine learning projects
Assist in testing, deploying, and monitoring models in production environments

Requirements

What you’ll need

Strong programming in Python or Node.js
Hands-on experience with LLMs, RAG, embeddings, and vector databases
Experience with cloud deployment and MLOps fundamentals
Familiarity with conversational AI platforms, including Dialogflow CX and GenAI playbooks
Knowledge of multimodal models, agentic AI, and model fine-tuning
Exposure to LangChain, LangGraph, and orchestration frameworks
Experience Level: Experienced Professionals

Benefits

Comp & perks

Meaningful projects using technology to solve real-world challenges
Continuous learning through internal academies, certifications and mentorship
Open access to digital learning platforms
Collaborative community of colleagues around the world