Top Artificial Intelligence Jobs in Mexico

Architect and develop secure enterprise applications and agentic AI workflows using React, TypeScript, Python, APIs, and MCP integrations. Implement structured outputs, workflow state management, human approvals, error recovery, authentication, RBAC, audit logging, evaluation, guardrails, tracing, and monitoring. Translate requirements into scalable solutions, build reusable configuration-driven components, maintain production-quality code and documentation, and mentor engineers while guiding technical direction.

Make outbound calls to US-based business owners and decision-makers on behalf of B2B technology companies. Use an AI-enabled sales platform, follow approved scripts, handle objections, and book qualified meetings for customer sales teams. Representatives receive coaching, complete training and certification, choose customer programs, and work flexible hours as independent contractors. Compensation is commission-based in USD, with no fixed schedule or employee benefits.

Designs, develops, and maintains ETL pipelines that extract, transform, and load data for analytics, data warehousing, and business intelligence. Ensures data accuracy, integrity, quality, and governance; optimizes and troubleshoots pipelines; documents data flows; and collaborates with business partners, architects, analysts, and developers. The role also supports migration of ETL jobs to Azure Data Factory and recommends improvements based on evolving data integration technologies.

Design and build production-grade agentic AI workflows and enterprise SaaS products. Develop agent orchestration, tool use, memory, context management, human approvals, and multi-agent collaboration. Integrate AI agents across development, testing, deployment, and support workflows. Establish standards for evaluation, observability, security, reliability, and governance. Mentor engineers, prototype capabilities, and convert successful experiments into scalable, secure products while driving agentic engineering strategy across teams.

Design and build production-grade agentic AI workflows and enterprise SaaS products. Develop agent orchestration, tool use, memory, evaluation, observability, security, and failure recovery patterns. Integrate AI agents across software development and operational workflows, move prototypes into scalable products, mentor engineers, and shape organization-wide agentic engineering practices.

Design and build production-grade agentic AI workflows and enterprise SaaS products. Develop agent orchestration, tool use, context management, memory, multi-agent collaboration, and human-approval workflows. Integrate AI agents across software development and operational processes, while establishing standards for evaluation, observability, security, reliability, and governance. Mentor engineers, guide technical strategy, and convert prototypes into scalable, secure, maintainable products.

Design and build production-grade agentic AI workflows and enterprise SaaS products. Develop agent orchestration, tool use, context management, memory, multi-agent collaboration, and human approval workflows. Integrate AI agents across development, testing, deployment, and support. Establish standards for evaluation, observability, reliability, security, and governance. Mentor engineers, guide technical strategy, and transform prototypes into scalable, secure, maintainable products.

Design and build production-grade agentic AI workflows and enterprise products. Develop reusable orchestration, tool-use, context, memory, evaluation, security, and governance patterns. Integrate AI agents across software development and operational workflows, moving prototypes into reliable, scalable systems. Mentor engineers, shape agentic technical strategy, and collaborate with product and engineering leaders to deliver secure, observable, user-focused solutions.

Design and build production-grade agentic AI workflows and enterprise products. Develop reusable patterns for orchestration, tool use, context, memory, multi-agent collaboration, evaluation, observability, security, and governance. Integrate AI agents across software development and operational workflows, moving prototypes into scalable products. Mentor engineers, establish engineering practices, and collaborate with leaders to deliver reliable, secure, user-focused solutions.

Designs and builds production agentic AI workflows and enterprise SaaS products. Responsibilities include orchestrating tools, services, data, and approvals; integrating AI agents across the software lifecycle; establishing evaluation, observability, security, and governance standards; moving prototypes into reliable production systems; mentoring engineers; and shaping organization-wide agentic development strategy.

Design and build production agentic AI workflows and enterprise SaaS products. Develop agent orchestration, tool use, context management, memory, multi-agent collaboration, and human-approval workflows. Integrate AI agents across development, testing, deployment, and support while establishing standards for evaluation, observability, security, reliability, and governance. Prototype and productionize capabilities, mentor engineers, improve productivity, and collaborate with product and engineering leaders to deliver reliable customer solutions.

Develop and support automation processes for automotive manufacturing at Valeo’s Querétaro plant. The role requires knowledge of PLCs, Python programming, and artificial intelligence applications to improve manufacturing operations and mobility technology production.

Supports AI governance projects by assessing risks and controls, documenting processes, gathering requirements, analyzing information, and synthesizing findings. Advises clients on regulatory compliance, risk management, internal controls, and governance improvements. The role involves applying AI/ML governance frameworks such as NIST and ISO, translating technical concepts for stakeholders, collaborating with diverse teams, and developing expertise in corporate governance and risk management.

Leads AI and data solution architecture for client transformation programs. Designs generative AI, data modernization, LLM, vector database, RAG, and LLMOps solutions; develops roadmaps and evaluation frameworks; advises executives; drives innovation; manages workstreams, client relationships, business growth, thought leadership, and team mentorship.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Designs, develops, and deploys enterprise Generative AI and LLM applications. Builds RAG architectures, agentic AI workflows, vector search and retrieval pipelines, REST API integrations, and cloud-native Azure services. Works with stakeholders and engineering teams to deliver secure, scalable solutions, supporting testing, deployment, monitoring, troubleshooting, and optimization. Requires strong Python, AI application development, Azure, search, API, and software engineering skills.

Designs and leads enterprise Salesforce solutions across industries in LATAM. Acts as a strategic advisor to business and technology leaders, translating requirements into scalable multi-cloud architectures, facilitating discovery workshops, guiding configuration and prototyping, and ensuring platform governance. The role partners with delivery teams, manages risks, mentors architects and consultants, supports proposals and presales, and leads executive stakeholder discussions. Strong Salesforce expertise, consulting experience, agile delivery knowledge, and English fluency are required; Spanish and Agentforce experience are preferred.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, and debugging support to colleagues. Develop reusable agent frameworks, starter kits, evaluations, documentation, and learning resources. Partner with internal teams on AI platform adoption, identify skill gaps, track learning outcomes, and escalate complex data, security, or production issues.

Build and debug LLM agents, integrations, and AI workflows using Python and APIs. Provide technical office hours, mentoring, code reviews, and debugging support; develop reusable agent frameworks, starter kits, evaluations, documentation, and learning resources. Partner with internal teams to identify skill gaps, deliver hands-on training, troubleshoot AI issues, and escalate complex data, security, and production problems while promoting safe, maintainable AI adoption.

Build and debug LLM agents, integrations, and AI workflows using Python, APIs, and LLM SDKs. Provide technical office hours, mentor colleagues, troubleshoot prompts and tool calls, develop reusable starter kits and documentation, and lead hands-on AI training. Partner with internal teams on escalations, data safeguards, evaluations, and platform improvements while helping employees independently adopt and maintain AI solutions.

Build and deliver technical enablement for client engineering teams adopting Claude tools. Responsibilities include leading deep-dive sessions, hosting office hours, answering technical questions, debugging code, reviewing learning paths and assessments, scoring readiness evaluations, creating remediation plans, and documenting solutions. The role requires production experience with Claude API and Claude Code, knowledge of MCP and agent patterns, enterprise code debugging, cloud AI platform experience, and advanced English communication.

Build and maintain conversational AI agents and sales intelligence data products for revenue, pipeline, bookings, discounting, and territory analytics. Integrate enterprise systems including Salesforce, Snowflake, Databricks, BigQuery, and SharePoint; develop semantic models, retrieval pipelines, vector indexes, and evaluation datasets. Partner with sales, finance, IT, and security teams to scope solutions, validate quality, support governance, document capabilities, and enable user adoption.

Architect and develop scalable backend systems, APIs, AI-assisted services, and full-stack SaaS features using Scala, React, AWS, and related technologies. Build agent-driven development workflows, internal engineering tools, prompts, and AI-native coding practices. Own features from ideation through production, optimize performance, and help establish engineering standards. The role offers significant autonomy in a remote, async-first environment and may eventually lead a small engineering team.

Architect and evolve backend systems and scalable APIs while building React frontend features for an AI-native SaaS platform. Develop agent-driven workflows, internal engineering tools, prompts, and AI-assisted services using tools such as Claude Code and Codex. Own features from ideation through production, optimize performance, establish AI-native development practices, and eventually lead a small engineering team.

Architect and evolve backend systems, scalable APIs, AI-assisted services, and full-stack SaaS features. Build React frontends, develop agent-driven engineering workflows, create internal tooling and prompts, optimize application performance, and establish AI-native development practices. The role requires end-to-end ownership, strong system design, asynchronous communication, and eventual leadership of a small engineering team.