Top Artificial Intelligence Jobs in Mexico

Build and own backend services, APIs, data models, and algorithmic or AI-driven business logic using Python and Django. Responsibilities include designing relational schemas, implementing RESTful endpoints, optimizing queries and performance, applying caching, translating product ideas into technical designs, and contributing to engineering patterns within a distributed startup team.

Own end-to-end delivery of complex AI client projects, translating evolving requirements into project scopes, delivery plans, and successful human data programs. Manage client relationships, cross-functional coordination, risks, quality, timelines, budgets, profitability, and delivery metrics. Partner with Sales, Operations, Product, and Engineering while improving scalable processes for AI project execution.

Leads USAA Life Insurance Company's risk management framework across enterprise, operational, model, data, and AI risk. Oversees risk governance, regulatory compliance, risk appetite, controls, operational resilience, and model validation while advising senior leaders. Builds high-performing teams, modernizes and simplifies risk programs, partners across business, Finance, Compliance, and Legal, and ensures alignment with life insurance regulations, NAIC frameworks, solvency requirements, and strategic objectives.

Leads full-stack engineering projects through hands-on development and coordination of internal and external resources. Provides production support for financial and operational systems, designs architectures, develops APIs and frameworks, and builds CI/CD pipelines. Performs data analysis and reconciliations, while integrating LLMs and AI-assisted workflows into internal tools, research pipelines, and developer tools.

Develop and support financial systems, dashboards, trade feeds, risk platforms, operational tools, and accounting systems. Design and implement APIs, frameworks, architectural improvements, and CI/CD pipelines. Analyze and reconcile data, lead engineering contributions, coordinate internal and external resources, and integrate LLMs and AI-assisted workflows into internal tools, research pipelines, and developer tooling.

Deploys, secures, and automates enterprise AI platforms including Claude, Microsoft Copilot, and Gemini. Owns connector and skill requests, configures permissions and access groups, reviews AI capabilities for security risks, and replaces manual configuration with Terraform and CI/CD pipelines. Partners with security, identity, governance, and business teams to ensure compliant, well-documented deployments across development, staging, and production environments.

Leads the vision, strategy, organizational development, and business performance of DEUNA’s agentic AI product organization. Oversees Product Managers, defines scalable AI agent orchestration capabilities, partners with engineering on platform architecture, and drives automation, evaluation, monitoring, safety, compliance, and enterprise adoption. The role also aligns product initiatives with growth, revenue, merchant needs, and market expansion.

Design, build, and maintain scalable backend services and RESTful APIs using Python and FastAPI. Lead backend architecture, optimize performance, ensure security, implement CI/CD and observability, mentor engineers, and apply AI-native practices including generative AI tools and prompt engineering throughout the development lifecycle.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Leads business analysis for enterprise software products hosted on Microsoft Azure. Elicits and documents requirements, creates user stories and acceptance criteria, supports Scrum delivery and backlog refinement, analyzes enterprise processes and integrations, and partners with product, engineering, architecture, and UX teams. Helps define AI-enabled capabilities, including intelligent search, conversational interfaces, agents, and automation, while addressing responsible AI, governance, security, and business outcomes.

Conduct approximately 50 outbound calls daily to US tax and accounting decision-makers, generate and qualify at least 10 opportunities monthly, manage pipeline activity in Salesforce, follow established sales methodologies, collaborate with Account Executives on lead handoffs, and participate in coaching. The role requires selling SaaS solutions powered by data and AI to CPAs and tax professionals.

Build and ship production software in client technology stacks, including APIs, services, data models, interfaces, automated tests, CI/CD, and failure handling. Use AI coding agents daily while reviewing all generated code. Work with relational and non-relational databases, third-party integrations, cloud deployments, infrastructure as code, and compliance requirements. Collaborate directly with clients in English and deliver maintainable, tested software end to end.

Build and operate production GenAI features and shared AI infrastructure, including RAG pipelines, LLM personalization, conversational agents, agentic workflows, evaluation frameworks, guardrails, MLOps, and observability. Own features from specification through deployment and on-call operations, evaluate model quality, integrate AI applications with business tools, document decisions, and mentor engineers.

Own email and lifecycle marketing for a new premium direct-to-consumer furniture brand. Responsibilities include campaign calendars, automated flows, segmentation, deliverability, list health, quality control, AI workflow implementation, cross-functional coordination, and translating marketing results into business insights. The role requires independent ownership, premium brand copywriting, ecommerce expertise, and strong attention to detail.

Leads the Advanced Manufacturing Engineering team and molding technology strategy for composite blade manufacturing. Directs process development, testing, validation, industrialization, automation, data analysis, technology roadmaps, supplier performance, and capital planning. Oversees molding processes including mold preparation, gel coat, fiber layup, resin infusion, curing, and bonding. Develops engineers, manages project portfolios, ensures technical documentation and change control, and drives safety, quality, cost, productivity, and operational excellence improvements.

Leads enterprise cybersecurity and core technology audit strategy, annual planning, audit execution, reporting, continuous monitoring, and regulatory readiness. Provides independent assurance and credible challenge across technology governance, cyber operations, cloud, resilience, third-party risk, identity, data protection, and emerging technology. Partners with executives, regulators, committees, and assurance functions while building and developing a high-performing audit team. Oversees risk-based coverage, quality, issue remediation, and strategic audit transformation through data-driven insights and automation.

Leads global commercial customer data governance, Customer 360 strategy, segmentation, data quality, reporting, and insight initiatives. Owns customer definitions and hierarchies across SAP and Salesforce, establishes governance and KPI standards, and aligns reporting and analytics investments with business value. Partners cross-functionally to improve data quality, reporting adoption, forecast accuracy, and AI readiness while driving change management and value realization across the commercial organization.

Develop product positioning, messaging, content, and go-to-market assets for an AI training data platform. Lead end-to-end product, vertical, DataLab, and commercial launches; translate complex AI and data concepts for technical buyers; create thought leadership, case studies, decks, webinars, videos, social content, and landing pages; and partner with Product, Sales, DataLab, Community, and business leaders to strengthen market narrative and support commercial growth.

Lead candidate experiences for the United States talent market from Mexico City. Responsibilities include candidate screening, shortlisting, interview coordination, communications, offer extensions, employer-brand representation, Workday data maintenance, stakeholder partnership, and recruitment process improvement. The role requires strong recruiting experience, U.S. market expertise, advanced English, applicant tracking system knowledge, and interest in using artificial intelligence and technology to improve hiring outcomes.

Lead and develop seven Data Specialists in Mexico City, ensuring high-quality customer data services, strong communication, and responsive delivery. Own team metrics, quality reviews, capacity planning, and operational priorities. Coach team members through complex data challenges, lead strategic efficiency initiatives and AI adoption, and collaborate with Sustainability Advisory, Customer Success, and Project Management. Help establish an inclusive, high-performance culture across the Mexico City office.

Lead design, evolution, and governance of enterprise data architecture. Define data models, integration patterns (batch and real-time), and technology choices. Drive metadata management, lineage, data quality, privacy, and adoption of cataloging tools. Collaborate with engineering, analytics, and business teams to translate requirements into scalable cloud data solutions and promote standards, best practices, and AI-native development.

Lead AI transformation across Sales, Marketing, Customer Success, Partnerships, and Revenue Operations. Identify high-value opportunities, design and deploy production AI agents, agentic workflows, automations, integrations, and enterprise solutions, then drive adoption and measure business impact. Partner with functional leaders, make sound decisions about AI suitability, redesign operating models, improve reliability and governance, and mentor colleagues while establishing reusable standards across the organization.

Build and own production AI features serving millions of students. Design debuggable agent, tool, and orchestration systems; establish evaluation practices; choose between LLMs, classic machine learning, and deterministic solutions; make defensible architecture decisions; and improve engineering standards through reviews and technical leadership.

Design, build, and ship production-grade full-stack software integrated with agentic AI systems and LLM APIs. Use AI-assisted development throughout the software lifecycle, evaluate AI-generated outputs, and manage reliability, latency, token, and cost considerations. Own delivery from design through production support, track AI productivity KPIs, collaborate in Agile client environments, and contribute reusable components, knowledge bases, and AI tooling standards.

Lead design, development, and deployment of AI/ML solutions for manufacturing: create models for optimization, predictive maintenance, quality analytics; build data pipelines, scalable architectures; apply computer vision, NLP, and time-series methods; integrate with production teams; mentor engineers and drive Industry 4.0 initiatives.