Top Artificial Intelligence Jobs in Mexico City

Author and refine curriculum for experienced engineers transitioning to system architecture roles: validate syllabus, define learning objectives, produce and fact-check AI-generated lessons, design hands-on projects and rubrics, and iterate with LX designers. Part-time, remote, milestone-based delivery over a 4–6 month project.

Teach and mentor students in AI automation through 1:1 calls, Q&A sessions, webinars, project reviews, written guidance, and mock interviews; create resources and foster a collaborative learning community.

Perform outbound prospecting (cold calls, email, LinkedIn) to identify and qualify B2B AI SaaS leads, book discovery calls and demos, maintain CRM records, collaborate with marketing and sales, hit monthly KPIs, and stay current on AI/SaaS trends.

Design, build, and scale AI-driven automation and CRM systems using GoHighLevel and tools like n8n/Make/Zapier. Integrate platforms via APIs and webhooks, create multi-step workflows for lead gen, sales, and operations, maintain internal infrastructure, troubleshoot deployments, and produce SOPs and client-facing documentation.

Design, build, and deploy AI-powered automation workflows using LLM APIs and workflow tools (n8n, Zapier, Go High Level). Integrate APIs/webhooks, debug and optimize workflows, and evolve internal scalable AI infrastructure. Collaborate with operations to gather requirements and iterate, while researching new AI tools and improving system reliability.

Design, build, and optimize GoHighLevel automations and AI-driven workflows across client accounts. Rigorously test automations, document SOPs, manage multiple projects, collaborate with teams and clients, and improve systems for scalability, efficiency, and performance.

Design, develop, and deploy AI-powered automation systems using Claude Code and n8n. Build scalable workflows and pipelines, prototype with the CTO, troubleshoot and optimize automations, document processes, and work directly with clients to gather requirements and recommend solutions that improve operational efficiency.

Build and maintain Python/FastAPI backend services and APIs, implement React frontend components, integrate MongoDB and AWS, collaborate via GitHub, participate in agile sprint planning, and work with AI and product teams to improve scalability and reliability.

Lead design, build, and scale client-facing and internal automation systems. Create advanced workflows (GoHighLevel, n8n/Make/Zapier), integrate platforms via APIs/webhooks, document SOPs, advise clients, maintain CRMs and troubleshoot live systems for reliability and scalability.

Drive end-to-end high-ticket sales in AI and automation: prospect, conduct discovery calls, nurture leads, manage pipeline in CRM, and close deals. Execute high-volume outbound campaigns using cold calling, email, and LinkedIn while meeting revenue targets and refining outreach with leadership.

Design, develop, and deploy scalable AI/ML solutions using LLMs and generative AI. Build RAG systems, chatbots, automation workflows, and production model pipelines; collaborate with product and engineering teams, monitor and improve model performance, and document systems.

Outbound-focused SDR role selling AI SaaS: prospect via calls, email, and LinkedIn; qualify leads, engage decision-makers, book demos for AEs; maintain CRM records and meet monthly KPIs while collaborating with marketing and sales.

Design, build, and maintain AI-driven automations and lightweight internal tools using Make.com, LLMs, APIs, and basic code. Integrate platform data (Shopify, Google Ads, Meta, Looker Studio), document systems, train staff, and research emerging AI tools to boost team efficiency and scale operations.

Manage and deploy AI-driven automation and workflows, onboard clients, translate requirements into actionable automations, design prompts for LLMs, troubleshoot and iterate systems, coordinate with AI engineers, and track project timelines and performance.

Lead pre-sales and delivery of OpenShift AI solutions: design and implement enterprise AI/ML platforms, drive MLOps adoption, architect training/serving/inference at scale, support Generative AI and LLM deployments, ensure governance, automation, and alignment with business and IT strategies.

As an AI Engineer, you will develop and implement internal automation workflows, integrating various tools and APIs to improve productivity in design and development processes, primarily using LangChain and related frameworks.

As the AI Agents Solutions Architect at Kraken, you'll design and build an AI-native finance operating system, automating finance operations and ensuring compliance in a regulated environment.

The Senior AI Engineer will develop AI-driven solutions using LLMs, focusing on prompt engineering, integration, research, and data utilization for payment systems.

Lead Oyster's data platform and AI strategy, centralize AI initiatives into production systems, build scalable data and MLOps infrastructure, modernize knowledge architecture and semantic search, partner cross-functionally to embed AI into products and workflows, enforce tooling standards and governance, and drive organizational adoption and change management for AI-assisted operations.

Lead development of AI-assisted engineering tooling and infrastructure so agents can autonomously run in cloud dev environments, access CI/CD and internal systems, execute tests, and improve developer productivity through integrations, documentation, and enablement.

Lead AI-first HR operations by auditing HR workflows, designing and deploying AI tools, building people analytics dashboards (headcount, turnover, compensation, DIB), driving adoption, and partnering with HR teams to scale data-driven, measurable solutions.

The role involves evaluating AI-generated content, generating original content, and providing structured feedback informed by local cultural insights while working flexibly as an independent contractor.

Lead client-specific QA programs for generative AI projects: set sampling and acceptance thresholds, run root-cause analyses and CAPA, design training and certification, maintain dashboards and KPIs, manage escalations, standardize SOPs, pilot automations, coach annotators, and ensure secure data handling across distributed teams.