Top Artificial Intelligence Jobs in Mexico

Design, build, and scale production-grade, stateful autonomous AI agents; integrate agents into payments, fraud, and core product systems; implement LLMOps, observability, and evals-as-code; optimize RAG/vector DB pipelines for performance and cost; handle containerization, CI/CD, and post-launch stability; mentor engineers and drive best practices.

Design, build, and scale production-grade, stateful autonomous AI agents for a WhatsApp-based fintech. Implement LLMOps, observability, RAG/vector DBs, containerized deployments (Docker/Kubernetes), eval pipelines, cost/performance optimizations, and mentor engineers to ship robust, tested agentic systems integrated with payments, fraud, and core product infrastructure.

The AI Engineer will design, build, and scale autonomous AI agents, optimizing LLM capabilities and integrating them into the product, ensuring functionality across financial services.

Design, build, and scale production-grade, stateful AI agent systems integrated into core product flows. Implement LLMOps, observability, containerized CI/CD deployments, RAG/vector DBs, evaluation pipelines, performance/cost optimizations, and mentor junior engineers.

Lead a team of ML engineers, manage client engagements, and provide strategic advisory on ML architecture while driving pre-sales. Ensure team growth and quality of deliverables.

Design, build, and productionize stateful autonomous AI agents for a WhatsApp fintech product. Implement LLMOps, observability, containerized deployments, evaluation pipelines, performance/cost optimizations, and mentor junior engineers.

Evaluate the quality of AI-generated conversations by auditing chatbot interactions based on naturalness, utility, and audio quality, ensuring adherence to project guidelines.

Perform part-time remote data tasks to improve Spanish (Mexico) AI outputs: collect, evaluate, annotate, and tag content across audio, video, images, and other data; perform pairwise comparisons and counting tasks. Flexible schedule, immediate start.

Lead technical client engagements in machine learning and AI, shape strategy, influence architecture, and ensure high-quality outcomes for clients by leveraging AWS services.

Design and implement scalable Generative AI architectures, build Python services, integrate LLMs, oversee deployment operations, and evaluate emerging technologies.

Design, implement, and maintain automated test suites using Playwright. Integrate tests into CI/CD pipelines, mentor team members, and enhance test reliability.

Design and build AI systems using LLMs and Agentic AI solutions, develop multi-agent systems, implement prompt engineering, and ensure AI governance while collaborating on scalable applications.

The Data Scientist will develop machine learning models, apply optimization techniques, analyze complex datasets, and collaborate with cross-functional teams to deliver solutions tailored to life sciences and related industries.

The Senior AI/ML Engineer will architect and develop AI/ML solutions, collaborate with teams, and ensure deployment compliance, especially in healthcare.

The MCP Solutions Architect supports client integrations, troubleshoots technical issues, and bridges customer engineering and internal AI engineering teams, focusing on AI adoption.

The AI Engineer & Data Engineer will develop data pipelines, ensure data quality, and integrate AI into workflows for performance reporting and billing reconciliation.

Serve as a client-facing support engineer for a fintech SaaS platform, triaging and resolving issues, using AI tools to enhance responses, logging and escalating tickets in Jira, executing basic corrective actions, and driving change requests from recurring feedback.

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

The Principal AI Engineer will build AI platforms, develop agent harness infrastructures, implement evaluation frameworks, automate CI/CD pipelines, and ensure the quality and security of AI agents, leveraging multi-turn reasoning and agent orchestration.

The Lead AI Engineer will design and implement AI and ML systems, focusing on building production-grade models, data pipelines, and feedback loops to enhance agent performance and business outcomes.

The Lead AI Platform Engineer will develop and maintain the ML/AI platform infrastructure, focusing on CI/CD pipelines, secure cloud infrastructure, and AI tooling, while collaborating with engineering teams to enhance developer workflows and ensure platform reliability.

Use advanced SQL and Python to analyze fleet-scale manufacturing, test, and operational data. Build KPIs, statistical analyses, dashboards, and AI-driven automations (anomaly detection, recommendations). Partner with engineering, product, and suppliers to improve data quality, embed AI into NPI and manufacturing processes, and deliver executive summaries and decision-ready operational insights.

Embed with client teams to diagnose operational friction, redesign operating models, and rapidly build AI-powered proofs of concept. Map workflows, establish governance and measurement frameworks, drive adoption through prototypes and change management, and scope engagements to capture measurable business value.

Map and redesign creative workflows, build and integrate AI-powered content pipelines (LLMs, agents, automation) into production ecosystems, establish governance and adoption, deliver proofs-of-concept and production solutions, and enable client teams for sustained creative transformation.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.