73 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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You will design, build, and deploy scalable applications that consume machine learning models while working on internal AI software engineering and data science projects. You will also collaborate with senior developers to implement solutions across various domains like connected vehicles, manufacturing, and supply chain analytics.
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, build, and deploy advanced AI solutions using LLMs, RAG, and agentic architectures to automate workflows and process unstructured data. Collaborate with cross-functional teams to deliver production-ready AI applications across various cloud platforms.
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.
You will spearhead the development of innovative AI-driven solutions and deploy scalable products leveraging LLMs to support the payment orchestration platform. Additionally, you will design and optimize prompts while collaborating with cross-functional teams to integrate AI features into existing payment workflows.
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Translate approved AI architectures into secure, scalable, and maintainable production-ready systems using LangChain and LangGraph. Build and deploy LLM-powered applications, agentic workflows, and RAG solutions while ensuring operational readiness and performance.
You will design and build AI agents and tools to automate engineering workflows, including test generation and code review assistance. Additionally, you will collaborate across teams to improve local development environments and reduce friction in CI/CD pipelines.
You will collaborate with cross-functional teams to translate complex business needs into scalable, production-ready AI solutions. The role involves developing LLM-powered applications, implementing responsible AI controls, and managing model evaluation frameworks.
The Senior AI Engineer will design, architect, and deploy production-grade Generative AI solutions, including LLM-based systems and agentic workflows. They will collaborate with cross-functional teams to optimize AI applications for scalability, reliability, and safety while implementing best practices for model performance.
The Senior Solutions Engineer will partner with sales and leadership to design scalable AI data and model evaluation workflows for enterprise clients. They are responsible for translating complex customer requirements into actionable pilot plans, technical proposals, and delivery frameworks.
Design, develop, and implement scalable Generative AI solutions using Python and LLMs. Manage cloud infrastructure as code and integrate AI components into productive environments.
Develop, integrate, and maintain conversational AI and IVR solutions using Dialogflow ES and Genesys Cloud. Collaborate with cross-functional teams to optimize customer journeys, bot performance, and backend system integrations.
The Junior AI Engineer will support the design, development, and deployment of AI-powered products and features using LLMs. They will also build APIs, manage integrations, and monitor pipelines to ensure reliable and scalable AI solutions.
The role involves leading the development of production-ready features within a mature Ruby on Rails monolith while integrating AI capabilities. You will be responsible for building shared AI infrastructure, establishing safety guardrails, and guiding the engineering team on practical AI adoption.
Design, develop, and implement enterprise-grade solutions using Generative AI, Machine Learning, and Intelligent Automation on Microsoft Azure. Manage data pipelines, ensure secure AI governance, and oversee the deployment and operational performance of AI models in production.
The Lead AI Engineer will design and deploy production-grade agentic systems that execute real-world actions with human-in-the-loop controls. They will own the end-to-end agentic function, including architecture, retrieval engineering, and the growth of the technical team.
Design and deploy AI-driven systems to detect and neutralize security threats, fraud, and intrusions. Build high-performance microservices and real-time data pipelines to automate incident response and protect member data.
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Design, develop, and operate enterprise-grade Generative AI and Machine Learning solutions on Microsoft Azure. This includes building AI agents, copilots, and RAG solutions while ensuring secure and governed production operations.
Own the scale and reliability layer of GTM AI agents, transitioning them from prototypes to production-grade systems. This includes managing observability, deployment pipelines on GCP, and database administration for the agent fleet.
Drive the modernization of the core platform by tackling technical debt and owning architectural decisions for cloud-based systems. Design and implement scalable AI and agentic workflows using LLMs while ensuring high code quality and security.
Own the end-to-end delivery of production AI systems, including design, implementation, and deployment. Collaborate with senior engineers on complex builds and manage direct communications and demos with clients.
Own the end-to-end delivery of production AI systems, including design, implementation, and deployment. Collaborate with senior engineers on complex builds and manage direct communications with clients.
Design and build enterprise-grade AI solutions and automation workflows using AWS Bedrock and modern cloud technologies. Integrate AI agents and RAG pipelines with enterprise systems like ServiceNow and SAP to automate complex business processes.
Develop and maintain the Champion AI agent platform while enabling partner business units to build and deploy their own agents. This includes designing system prompts, managing agent infrastructure, and implementing production-ready agent use cases.
The role involves embedding with client teams to diagnose operational friction and redesign workflows using AI-powered solutions. You will build functional prototypes and establish operating models to drive technology adoption and measurable ROI.
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Map and redesign creative workflows to identify inefficiencies and build AI-powered pipelines for production environments. Establish governance frameworks for brand safety and train teams to adopt new AI-driven operational models.
Design, build, and scale autonomous AI agents to evolve a WhatsApp-based platform into a multi-agent system for complex financial tasks. Lead the integration of these agents into core product infrastructure while establishing LLMOps, observability, and evaluation pipelines.
Design and implement backend services and APIs using Python and Django while modeling clean database schemas. Implement complex algorithmic logic for scoring, ranking, and routing to turn product ideas into technical endpoints.
Design, develop, and deploy Generative AI solutions using LLMs and multimodal technologies. Build scalable AI applications and optimize RAG architectures while ensuring compliance with AI governance and security standards.
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