AI Engineer
Design and deliver production-ready GenAI solutions, including Document AI, OCR, and Agentic AI workflows. Implement LLM and RAG architectures while leveraging Python and cloud platforms to build scalable enterprise applications.
Remote AI Engineer Jobs in Mexico
166 AI Engineer jobs in Mexico available for remote work from home. Apply for positions such as AI Engineer, AI ENGINEER, AI Engineer and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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Design and deliver production-ready GenAI solutions, including Document AI, OCR, and Agentic AI workflows. Implement LLM and RAG architectures while leveraging Python and cloud platforms to build scalable enterprise applications.
The AI Engineer will design, build, and optimize machine learning models and AI solutions to support enterprise automation and intelligent decision-making. They will also collaborate with cloud and engineering teams to deploy models and manage data pipelines.
You will own end-to-end AI workflows for financial processes, designing and deploying agentic systems that integrate with the company's core platform. You will also be responsible for measuring, monitoring, and improving these AI systems to ensure reliability and performance in a production environment.
Design, build, and deploy production-ready GenAI solutions including RAG systems and agentic workflows. Collaborate with cross-functional teams to ensure AI reliability, performance, and scalability in enterprise environments.
You will build tools to evaluate agent behavior, analyze traces, and implement features for automatic self-improvement. Additionally, you will run experiments on Mastra primitives to optimize configuration and performance.
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Design, develop, and deploy multi-agent AI systems and RAG pipelines for reasoning and task execution. Collaborate with engineering teams to integrate data sources and implement observability frameworks for model performance monitoring.
The AI Engineer will design, develop, and maintain production-grade AI services and APIs using Python and modern orchestration frameworks. They will also build and optimize end-to-end RAG solutions while ensuring system quality through observability, evaluation, and performance monitoring.
The AI Engineer will lead the development and deployment of internal AI agents, intelligent search tools, and automated business workflows. They are responsible for integrating AI models with existing SaaS platforms and establishing robust infrastructure, security, and governance for AI initiatives.
Design and deploy production-ready conversational agents for voice and WhatsApp to optimize conversion and resolve customer issues. Implement end-to-end Voice AI, conversational memory systems, and integrate agents with internal tools and CRMs via APIs.
Design, develop, and deploy scalable machine learning and AI systems for real-world applications. Manage data pipelines, optimize custom models, and maintain MLOps workflows using cloud platforms.
The AI Engineer will design, build, and deploy production-ready AI features including LLM-backed workflows and retrieval systems. They will also own the data path, define evaluation metrics, and monitor deployed systems for quality and performance.
Design, build, and deploy production-ready AI applications including RAG-powered solutions and conversational agents on major cloud platforms. Collaborate with cross-functional teams to automate insurance workflows such as claims and underwriting.
The candidate will serve as an AI Engineer. Specific duties are not detailed in the posting.
Design, develop, and implement Generative AI solutions using Python, LLMs, and RAG. Integrate AI capabilities with APIs, databases, and enterprise legacy systems within AWS environments.
Design and develop machine learning and deep learning models to solve complex engineering problems in the semiconductor space. Collaborate with multidisciplinary teams to deploy AI solutions and ensure model robustness through evaluation and monitoring.
Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written feedback and video explanations to help define high-quality engineering standards.
Design, build, and operate backend services and cloud infrastructure on GCP to support the App Foundry and employee AI platform. Provide technical guidance, reusable integration patterns, and operational support to product teams while ensuring production readiness and security.
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You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. You will collaborate with AI scientists to discover new data sources and ensure the scalability and quality of training datasets.
The Applied AI Engineer will design, build, and deploy LLM-powered features and agents from prototype to production. They will also be responsible for implementing RAG, monitoring system performance, and collaborating with product teams to define technical criteria.
You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. This involves discovering new audio data sources and collaborating with AI scientists to improve data quality and system scalability.
You will build and optimize large-scale data pipelines to support AI model training and next-generation product development. You will also collaborate with AI scientists to discover new data sources and ensure high-quality, scalable data processing.
Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written feedback and video walkthroughs to help define high-quality AI-assisted development standards.
Evaluate the quality of AI-generated coding interactions to ensure they reflect strong engineering judgment and reasoning. Provide clear, opinionated written and video feedback to help define high standards for AI-assisted development.
Evaluate AI-generated coding interactions to assess the quality, reasoning, and engineering judgment of modern coding agents. Provide clear, opinionated written and video feedback to help define high-quality standards for AI-assisted development.
Evaluate AI-generated coding interactions to assess the quality of reasoning, engineering judgment, and overall helpfulness. Provide clear, opinionated written and video feedback to help define high-quality standards for AI coding agents.
Evaluate the quality, reasoning, and engineering judgment of AI-generated coding interactions. Provide clear, opinionated written and video feedback to help define high-quality standards for AI coding agents.
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Design, develop, and deploy LLM-powered features and internal AI tools to drive business value for healthcare organizations. Collaborate with cross-functional teams to implement RAG pipelines, agentic workflows, and ensure production-grade reliability and security.
Lead the development and deployment of Agentic AI solutions while building and scaling enterprise AI platform infrastructure. Provide technical leadership, mentor teams, and integrate AI platforms with internal systems to support business use cases.
You will bridge the gap between functional design and technical implementation by creating low-level designs and prototype code for agentic AI systems. This involves running independent proofs of concept, proving integration patterns, and documenting solutions for a distributed development team.
Design, develop, and deploy AI-driven applications and workflows on the ServiceNow platform. Interface with stakeholders to gather requirements and manage the full application development lifecycle including coding, testing, and troubleshooting.
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