Top Artificial Intelligence Jobs in Mexico

Architect and evolve backend systems and scalable APIs while building React frontend features for an AI-native SaaS platform. Develop agent-driven engineering workflows, internal tools, prompts, and AI-assisted services; optimize performance across the stack; and define AI-native software development practices. The role owns features end to end, operates autonomously in a remote async culture, and may eventually lead a small engineering team.

Leads application development and systems analysis while designing, building, and testing AI-enabled software prototypes. Partners with technology and management teams to deliver system enhancements, define architecture-aligned solutions, establish coding and testing standards, resolve complex technical problems, and assess risks. The role also mentors developers and analysts and emphasizes rapid full-stack and data-oriented development using cloud AI accelerators, Claude, Amazon Bedrock, API tooling, and prompt engineering.

Design and maintain secure, scalable data pipelines and modern data lakes and warehouses for insurance use cases. Integrate structured and unstructured data, automate ingestion and transformation, implement data quality, governance, lineage, and regulatory compliance, and prepare datasets for analytics and machine learning. Collaborate with data scientists, architects, and stakeholders while documenting data solutions. Apply AI-native engineering practices, including generative AI coding tools, prompt engineering, AI-assisted development, and GenAI output validation.

Develop software, data solutions, dashboards, and business web applications for engineering, operations, and finance teams. Maintain and optimize applications, improve software quality, apply Agile/Scrum practices, document projects, and manage assigned tasks. The role requires full-stack development, scripting, SQL, version control, automation, and strong analytical and communication skills. Familiarity with Java Spring Boot, .NET Core, Docker, Linux, AI tools, and AWS infrastructure as code is preferred.

Leads the architecture, implementation, and deployment of cloud-native, AI-powered healthcare solutions. Defines scalable designs, evaluates technologies, oversees delivery, conducts architecture reviews, and ensures alignment with business and technical requirements. Partners with product, engineering, data, IT, and business stakeholders while mentoring architects and guiding modernization initiatives. Requires extensive software architecture, cloud, full-stack development, database, DevOps, AI/ML, and healthcare workflow experience.

Lead backend and cloud architecture using Ruby and Google Cloud, guide engineering teams, manage APIs and Kubernetes deployments, and deliver secure, scalable enterprise solutions. Responsibilities include high availability, disaster recovery, observability, cost optimization, CI/CD, DevOps, and AI integrations using Vertex AI, Gemini API, and agentic frameworks.

Leads enterprise workflow reinvention and systems-of-work design across business and technology teams. Designs human-AI collaboration, automation opportunities, controls, decision paths, and implementation-ready workflow artifacts. Facilitates senior stakeholder workshops, connects business intent to architecture and delivery, promotes reusable enterprise patterns, and helps establish systems design practices. Measures engagement impact through business outcomes, case studies, and executive storytelling.

Drives procurement digital transformation through advanced analytics, data modeling, Power BI reporting, systems improvement, automation, sustainability data management, and global process governance. The role develops predictive models for spend, savings, supplier risk, forecasting, and anomaly detection; evaluates SAP and supplier-management technologies; leads transformation projects; and partners with Procurement, Sustainability, IT, Data, and Finance to improve decision-making, compliance, adoption, and process efficiency.

Leads end-to-end private markets data management, including ingestion, extraction, validation, mapping, quality controls, and release to downstream systems. Designs scalable workflows using automation and AI, governs enterprise data mappings, manages onshore, offshore, and vendor teams, and partners with engineering, analytics, and product teams. Owns operational KPIs, resolves data quality issues, and drives process improvements across investment, finance, reporting, and client-facing functions.

Design, configure, and scale secure AI solutions that improve productivity, automation, decision-making, and workflow efficiency. The role identifies high-impact use cases, translates business needs into AI agents and automations, collaborates with IT, data, and security teams, runs pilots through production, measures adoption and business impact, and supports enablement through documentation, training, and reusable solution patterns.

Supports customer security engagements across architecture design, proof of concepts, and production deployments. Helps deploy and validate threat detection, alerting, logging, incident workflows, identity, endpoint, cloud, and monitoring capabilities. Creates reusable security documentation and runbooks, interprets security alerts, supports risk reduction, and collaborates with customer technical teams, engineers, and senior architects. The role includes training, certifications, and hands-on development of secure cloud and AI solutions.

Build and deploy AI agents and automated workflows for operational teams using Microsoft Copilot Studio, Power Automate, APIs, and related Microsoft AI technologies. Analyze business processes, translate requirements into production solutions, integrate systems, prototype with end users, and maintain compliant, monitored automations. Support adoption through training and continuous improvement while ensuring enterprise data, security, and governance standards.

Design, implement, test, debug, and evaluate production-grade Rust backend systems for AI training projects. Responsibilities include API and persistence development, asynchronous and concurrent processing, performance optimization, reliability analysis, architecture review, reproducible test environments, containerized workflows, and technical documentation. The role requires identifying correctness, scalability, and failure-handling issues while delivering high-quality implementation artifacts and meeting weekly project deadlines.

Freelance graphic design professionals rate and edit supplied design samples for AI training. They evaluate typography, composition, color, layout, hierarchy, execution, and polish using detailed rubrics, identify flaws, and revise slides for visual cohesion. The role is fully remote, project-based, and paid per accepted task. Candidates need design or arts education, at least two years of relevant experience, intermediate English, strong design fundamentals, and exceptional attention to detail.

Lead the strategy, roadmap, launch, adoption, and ongoing improvement of an AI-powered clinical data insights platform. Partner with Data Science, Engineering, and application teams to create reusable capabilities, define platform metrics, prioritize investments, evaluate build-versus-buy decisions, and align senior stakeholders across clinical, technical, and business objectives.

Build and operate production generative AI systems, including retrieval pipelines, tool-using agents, evaluation suites, guardrails, and reliable cloud deployments. The role covers RAG, agentic workflows, structured outputs, prompt-injection defense, observability, CI/CD, cost and latency optimization, and production troubleshooting. Engineers work directly in client environments and explain technical trade-offs in English. Experience with vector databases, agent frameworks, LLM evaluation, Azure or AWS, Docker, and infrastructure as code is required.

Lead the architecture, development, and production deployment of generative AI solutions using LLMs, transformer models, RAG pipelines, and agentic workflows. Design scalable cloud deployments, establish prompt engineering and evaluation practices, implement Responsible AI guardrails, and monitor model performance. Collaborate with engineering, product, data science, and MLOps teams to move solutions from prototypes to production. Mentor engineers and promote maintainable Python development, experimentation, KPI definition, and data-driven decision-making.

Lead the design, development, and production deployment of generative AI solutions using LLMs, transformer models, RAG pipelines, and agentic workflows. Build scalable multi-step AI agents, implement responsible AI guardrails, optimize cloud deployments, establish evaluation and monitoring practices, and collaborate with engineering, data science, MLOps, and product teams. Provide technical leadership while promoting maintainable Python code, experimentation, and data-driven decision-making.

Build and deploy production-grade generative and agentic AI applications using Python or JavaScript/TypeScript, LangChain, LangGraph, RAG, vector databases, Docker, Kubernetes, and cloud platforms. Responsibilities include developing workflows and integrations, implementing evaluation and observability systems, optimizing quality and performance, applying security and responsible AI practices, maintaining CI/CD processes, and supporting production operations. The role also involves technical documentation, customer communication, code reviews, mentoring, and collaboration with architecture, engineering, platform, security, data, and product teams.

Lead the engineering and SaaS operations organization across people, architecture, quality, delivery, reliability, security operations, and cloud costs. Drive AI-assisted development adoption, engineering experimentation, legacy modernization, roadmap delivery, production operations, and customer issue resolution. Manage Directors, architects, and distributed engineering teams while improving quality, uptime, cycle time, predictability, and customer outcomes across multiple products.

Leads Moderna’s Finance transformation agenda across the Record-to-Analyze value chain, including FP&A, Accounting, and Tax. Owns the transformation roadmap and portfolio, drives cross-functional initiatives from strategy through adoption and value realization, and advances finance technology, data, analytics, automation, and AI capabilities. Establishes governance, manages dependencies and risks, coordinates change and training, and influences Finance, Digital, HR, and business stakeholders to improve financial processes and decision-making.

Lead four product managers and own PayJoy’s end-to-end onboarding strategy across digital and in-store channels. Drive application, direct-to-consumer, enterprise, and offers/risk product areas; optimize conversion and acquisition costs; guide global versus localized solutions for new markets; ensure regulatory compliance; and collaborate with Engineering, Design, Data, Risk, and Operations to deliver scalable consumer fintech experiences.

Serves as a hands-on technical leader for emerging technology and AI experimentation. Designs prototypes, proofs of concept, pilots, integrations, and architecture evaluations; writes and reviews prototype code; troubleshoots complex technical issues; and develops reusable standards and recommendations. Collaborates with architecture, security, data, risk, legal, procurement, vendors, and delivery teams to transition validated concepts into scalable solutions. Mentors engineers and communicates findings, tradeoffs, risks, and adoption recommendations to technical and business stakeholders.

Designs, automates, and operates reliable AI platforms across Azure and AWS. Responsibilities include building observability and monitoring systems, leading incident response and root-cause analysis, developing AI-powered operational tools, automating workflows with Python and Bash, influencing platform reliability standards, and mentoring engineers. The role partners with engineering teams to improve production readiness, eliminate operational toil, and support scalable cloud infrastructure.

Own private capital financial data operations, ensuring accurate, timely, and high-quality client data. Use AI-assisted extraction and validation tools, investigate exceptions, improve quality frameworks, automate workflows, and strengthen operational controls. Partner with Product, Engineering, Data Operations, and Client Service teams to improve processes and client outcomes. Mentor junior colleagues, analyze KPIs, communicate insights, and drive continuous improvement across AI-enabled financial data operations.