AI Engineer
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.
68 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, 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.
The role involves designing clinical ontologies and building evaluation frameworks for production AI agents. You will also train, fine-tune, and maintain LLM-based systems within clinical workflows.
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You will design, develop, and deploy intelligent business solutions by combining Microsoft Power Platform with Generative AI technologies. You will work within multidisciplinary teams to build scalable, secure, and user-centric digital solutions that drive business transformation.
You will design and build AI-powered search and discovery experiences using multi-modal retrieval and agentic workflows. The role involves orchestrating LLMs, managing retrieval augmented generation, and acting as a technical interface between international teams.
The Senior AI Applications Engineer will design, integrate, and productionize Generative AI solutions and traditional software applications to drive business value. The role involves leading end-to-end delivery, mentoring junior developers, and collaborating with cross-functional stakeholders to ensure high-quality UX and technical compliance.
You will support the Enterprise AI Security Program by ensuring the safe and secure adoption of AI across internal chatbots and agentic workflows. This involves translating security policies into automated enforcement mechanisms and conducting risk assessments on new AI tools and protocols.
The engineer will build and ship features for the Champion platform while supporting Applied AI teams in deploying and operating agentic solutions. They will also design and develop custom agents for specific business use cases using an AI-native development workflow.
The engineer will build and maintain features for the Champion AI platform while supporting Applied AI teams in deploying agentic solutions. Responsibilities include developing agent infrastructure, improving developer tooling, and directly creating production-ready agents for business use cases.
You will build and maintain AI agents and LLM-based systems within Azure AI Foundry while ensuring the operational health of classical machine learning models. Additionally, you will implement LLMOps frameworks, contribute to platform integrations, and provide technical documentation and support for stakeholders.
The AI Engineer will design, build, and deploy intelligent solutions using modern Machine Learning, Deep Learning, and Generative AI technologies. They will collaborate with engineering, product, and data teams to turn complex business challenges into scalable AI-powered solutions.
You will design, build, and operate scalable cloud-native infrastructure while automating deployment workflows to improve developer productivity. Additionally, you will partner with security and engineering teams to define non-functional requirements and resolve complex production incidents.
Lead the technical architecture and evolution of an AI platform, including LLM-agnostic abstractions and agentic workflows. Ensure system reliability, security, and scalability through robust CI/CD pipelines, observability, and rigorous evaluation suites.
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.
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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.
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 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.
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.
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.
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 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.
Lead and mentor a team of three to eight software engineers, focusing on their professional growth and performance. Drive the delivery of value to customers by managing timelines, priorities, and handling customer escalations.
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