Top Artificial Intelligence Jobs in Mexico

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.

Own the post-sale customer journey from implementation through adoption, renewal, and expansion. Configure AI agents, workflows, prompts, integrations, call routing, notifications, and automation rules. Lead onboarding and implementation calls, troubleshoot technical issues, coordinate with engineering, identify churn and growth opportunities, and create playbooks to improve recurring implementation processes. Manage multiple B2B software customers while delivering measurable ongoing value.

Lead the architecture and technical delivery of scalable full-stack solutions using .NET Core and React. Design microservices, APIs, integrations, and event-driven systems while enforcing engineering standards for testing, CI/CD, security, and observability. Mentor engineers, guide architectural decisions, collaborate with product and cross-functional teams, assess technical risks, and incorporate AI-first practices into software delivery.

Design and build enterprise AI domain intelligence layers using ontologies, knowledge graphs, semantic models, agentic workflows, RAG, fine-tuning, and evaluation frameworks. Develop PoCs and convert successful architectures into scalable standards. Work hands-on with Microsoft Foundry and Azure for AI deployment, agents, evaluation, and safety. Define governance, reusable AI patterns, technical documentation, and architecture standards while influencing product and engineering teams across the organization.

As a Lead Software Engineer for AI, you will develop AI-powered systems, focusing on backend services, scalable production systems, and collaboration with cross-functional teams to deliver intelligent solutions.

Join a small cross-functional team to design, build, and operate an LLM-powered agent platform: orchestration, tool integrations, APIs, knowledge/memory infrastructure, trust & safety, evaluation/testing, and partner with Operations to translate workflows into automation. Mentor other engineers and deliver production-ready systems.

Analyzes human-generated data quality across AI programs using SQL, Python, statistical methods, and hands-on annotation review. Investigates quality issues, identifies failure patterns, measures impact, and develops recommendations. Supports quality pipelines, automated validations, rubrics, gold sets, calibration exercises, reporting, and dashboards. Collaborates with quality engineering, operations, delivery, and engineering teams to improve annotation design, training, tooling, and quality controls.

Build and operate reliable full-stack DevTools and AI reliability software using Vue.js, TypeScript, Node.js, Golang, PostgreSQL, ClickHouse, and AWS. Use AI coding agents throughout development, engage directly with developer and SRE customers, identify and solve reliability problems, own features from discovery through production, and participate in support and on-call rotations within a remote async-first team.

Support Mexico controllership by monitoring regulatory changes, maintaining internal controls, preparing local GAAP financial statement disclosures, supporting audits, and testing spreadsheet controls to mitigate risk and ensure compliance.

Designs and delivers firm-wide AI training, workshops, demos, coaching, and enablement content. Builds practical generative AI workflows using low-code or no-code tools, maintains prompt and training libraries, tracks emerging AI capabilities, and helps colleagues apply AI to business needs. The role requires strong communication, instructional design, organization, public speaking, and hands-on experience with generative AI tools.

Design and optimize production-grade Databricks lakehouse architectures using medallion layers, Spark, Delta Lake, and Unity Catalog. Govern data and AI assets, implement cloud security, tune performance, optimize costs, and develop advanced SQL and Python or Scala solutions. Preferred experience includes GenAI, MLflow, CI/CD, streaming, orchestration, data warehousing, BI integration, and Databricks certifications.

Leads and mentors an engineering team building scalable, reliable AI-powered solutions for PMG’s Alli marketing platform. Responsibilities include defining technical roadmaps, designing and deploying AI features, collaborating with product and cross-functional teams, guiding architecture and engineering practices, conducting code reviews, debugging, testing, and staying current with emerging AI and machine learning technologies.

This role focuses on building agentic outbound systems using AI and automation to improve efficiency, pipeline generation, and productivity within GO-to-Market strategies. Responsibilities include designing workflows, automating outreach, and collaborating with cross-functional teams.

Join a global contributor network supporting flexible AI projects. Depending on skills, contributors may annotate text, images, or audio; evaluate search results, advertisements, and chatbot responses; create prompts; and verify cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Develops data and AI solutions across SAP and non-SAP environments. Responsibilities include building ETL/ELT pipelines, cleansing and modeling data, integrating LLM and REST APIs, automating workflows with Python and SQL, supporting Databricks-based analytics, documenting integrations, and participating in ITSM support and on-call incident response.

Lead the architecture and development of scalable AI/ML pipelines and production systems, including LLMs and Generative AI. Mentor engineering teams, guide technical decisions, operationalize models, optimize performance and costs, and implement MLOps practices such as CI/CD, monitoring, versioning, and maintenance. Partner with executives, clients, product managers, data scientists, and software engineers to align AI roadmaps with business goals and integrate ethical AI capabilities into enterprise applications.

Build cloud and endpoint discovery capabilities for AI agents, assess their configurations, access, and security risks, and integrate findings into Delinea’s identity and attribution platform. The role requires backend development experience with cloud provider APIs, asset inventory systems, and existing platforms. Candidates should be able to solve ambiguous technical problems independently and may have experience with endpoint management, CSPM, CIEM, or AI agent authentication.