73 AI Engineer jobs in Mexico available for remote work from home. Apply for positions such as Principal Software Engineer, AI, Sr. Lead AI Engineer - 11509, Lead AI Engineer - 11510 and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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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.
Design and build the agent harness, including skill loading, tool invocation, and the execution sandbox. Own the cloud infrastructure, deployment, and evaluation pipelines to ensure the platform is secure and scalable.
Build and maintain the harness for supplier agents, including evaluation pipelines, context management, and sub-agent orchestration. Develop document parsing tools and wrap Coupa APIs as agent-callable CLI tools to handle end-to-end tasks.
Design and build server-side APIs and support tooling to manage large-scale data operations and backups. Utilize AI-assisted development tools to accelerate implementation while ensuring security, performance, and correctness.
Design and build server-side APIs for large-scale data operations and backup management. Utilize AI-assisted development tools to accelerate implementation while maintaining high standards for security and performance.
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You will build and scale an internal AI Accelerator Open Source Ecosystem Tracker using a full-stack Next.js application. Additionally, you will contribute to weekly digests and technical reports regarding upstream contribution health and cross-accelerator benchmark results.
Design and execute software development workflows leveraging AI agents for various tasks while building and improving internal tools and automation layers that enable these agents to operate effectively. The role involves end-to-end development, rigorous Git ownership, terminal-first execution, and managing secure developer operations across applications and infrastructure.
Design and maintain semantic context layers including knowledge graphs and structured context stores to inform AI agent behavior. Develop full-stack features and integrate RPA agents to enable context-aware automation and real-time visibility into agent activity.
The role involves designing, developing, and optimizing prompts for generative AI models while integrating these solutions into existing workflows. You will also collaborate with multidisciplinary teams to define AI use cases and ensure high-quality, accurate model outputs.
The Senior AI Software Engineer will design and develop enterprise-grade AI applications connecting backend systems with Large Language Models. Responsibilities include implementing RAG solutions, managing prompt lifecycles, and ensuring production-ready performance and scalability.
You will take end-to-end ownership of machine learning models, ensuring their reliable deployment and performance on physical hardware. Additionally, you will design and optimize real-time AI pipelines while collaborating with cross-functional teams and clients.
Design and maintain scalable backend services using Node.js and event-driven architectures. Integrate cutting-edge AI capabilities, including LLMs and RAG, into modern cloud applications.
Design and deploy AI-driven systems to detect and neutralize fraud, abuse, and security intrusions. Build high-scale real-time streaming pipelines and integrate security models into production via microservices.
Build and deploy AI-powered workflow solutions, including prompt architectures and automation flows, for non-technical business teams. Embed within functions like Finance and HR to map current workflows and implement AI leverage to improve operational efficiency.
Design, architect, and productionize full-stack applications and Generative AI solutions across various business functions. Lead high-impact initiatives, manage product roadmaps, and mentor junior developers to deliver scalable, high-ROI business tools.
Design and develop AI-powered automation solutions and agents using AWS Bedrock to streamline IT help desk workflows. Implement serverless applications and integrate enterprise platforms like Okta and Azure AD via REST APIs.
Design and implement production-ready AI features using LLMs, agent frameworks, and retrieval-augmented generation. Optimize AI architecture for cost, latency, and accuracy while managing voice AI and regulated data flows.
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Design and implement security safeguards for internal AI and agentic systems, focusing on runtime enforcement and identity controls. Establish observability, detection capabilities, and threat models to prevent unsafe AI-driven activity and protect developers.
Lead the architecture and evolution of backend and applied AI systems to deliver personalized shopper experiences. Design end-to-end AI services, create reusable building blocks for product teams, and provide technical leadership across multiple organizations.
Lead the end-to-end delivery of scalable backend services and applied AI systems to power personalized shopper experiences. Design and maintain data pipelines and integrate LLM capabilities into production-hardened systems with automated evaluation frameworks.
You will develop and maintain Model Context Protocol (MCP) tools to connect AI models with complex database environments. Additionally, you will refactor legacy code, expand test coverage for agentic coding assistants, and create technical documentation to support community adoption.
The DevOps Engineer will build and operate infrastructure for data pipelines and AI/ML workloads while developing CI/CD processes for model lifecycles. They will also partner with engineering teams to ensure the reliability, scalability, and performance of compute-intensive production systems.
Design, build, and maintain scalable backend services using Java and Python while managing distributed systems and data pipelines. Take full ownership of features throughout their lifecycle and collaborate with teams to implement AI-assisted development workflows.
You will own the full development lifecycle of features from conception to deployment while building scalable, maintainable, and customer-centric systems. You will also collaborate with cross-functional teams to design innovative solutions and contribute to team growth through interviewing.
Architect and deploy an AI-first Go-To-Market ecosystem by building custom integrations and agentic workflows to automate revenue operations. Optimize the tech stack to reduce vendor spend while enhancing pipeline generation and deal velocity across Sales, Marketing, and Customer Success.
Design and implement AI/ML solutions, ranging from proof of concept to production deployment, using traditional ML and modern generative AI techniques. Collaborate with cross-functional teams to build scalable, fair, and explainable AI systems within regulated healthcare environments.
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Design and maintain Azure-based cloud infrastructure and optimize CI/CD pipelines for multiple engineering squads. Focus on migrating legacy systems to cloud-native microservices while improving developer productivity and software delivery efficiency.
You will design, build, and scale production-grade Generative AI systems, including RAG pipelines and agent-based workflows. You will also develop Python-based backend services and ensure production readiness through CI/CD, monitoring, and performance optimization.
Design and develop scalable end-to-end applications using Angular, Node.js, and NestJS while integrating AI-enabled capabilities. Lead solution design discussions and collaborate with stakeholders to deliver maintainable cloud-based architectures.
Design and implement secure, stable backend systems and integrate AI-driven development agents to accelerate engineering velocity. Collaborate with cross-functional teams to deliver high-quality software while mentoring other engineers and reviewing code.
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