Top Artificial Intelligence Jobs in Mexico

Manage a portfolio of 5–10 enterprise accounts, drive multi-year SaaS deals and strategic consulting projects, build C-level relationships, execute full sales cycle (MEDDIC/BANT), collaborate with pre-sales and engineering, and maintain rigorous CRM and forecasting.

Lead design and delivery of generative and predictive AI solutions, build evaluation pipelines and responsible AI practices, assemble and model enterprise datasets, translate business needs into deployable AI products, and communicate results to stakeholders while driving R&D for GenAI and agentic AI in healthcare.

As a GTM Engineer / AI Engineer, you'll architect and scale systems for growth, develop AI workflows, and unify tools like Salesforce and HubSpot to enhance the customer lifecycle.

Design and lead scalable AI architectures for enterprise customer experience solutions. Translate AI capabilities into measurable business outcomes, build reusable frameworks and prototypes, align product and engineering, drive adoption, and establish lifecycle best practices including deployment, monitoring, governance, and optimization.

The ML Engineer will design, build, and operationalize AI systems for unstructured data, developing ML pipelines and collaborating with Data Scientists and Engineers to deliver scalable AI solutions.

Lead design and build of AI agents and integrations across pro-code and no-code platforms. Define automation patterns, integrate AI platforms with business tools via APIs, write Python for complex integrations and data transforms, evaluate agent performance, and guide architecture and tooling decisions while collaborating with VP of AI.

The AI Strategy Consultant will drive marketing strategy using AI, work with teams to implement solutions, and enhance customer outcomes.

The role involves rapid prototyping and architecting production LLM pipelines for AI in marketing workflows. Responsibilities include developing proofs of concept, making AI architecture decisions, building production data agents, and understanding user workflows for seamless AI experiences.

Design and build end-to-end web applications using .NET (Framework/Core) back-ends and modern front-end frameworks (React, Angular, Vue). Implement cloud-native solutions (Azure/AWS), optimize MS SQL databases, apply SOLID/design patterns, create unit/integration tests, lead Agile teams, set up CI/CD, use AI coding assistants, deploy AI features to production, and mentor peers in a collaborative multicultural environment.

Lead the definition and governance of enterprise AI architecture; design and oversee LLM-powered systems, RAG pipelines, agentic frameworks, and inference infrastructure; advise executives on AI strategy and risk; create reusable AI reference architectures and CoE artifacts; mentor engineers and integrate AI into DevOps/QA practices while ensuring regulatory and security compliance.

The Sr. AI Engineer will design and implement scalable machine learning models, optimize ML operations, and mentor team members while collaborating across departments to enhance AI-driven features.

The role involves onboarding new clients, configuring AI assistants, managing onboarding timelines, and providing client support to ensure successful implementations.

The Process Transformation Lead optimizes business processes across Revenue functions, implementing AI and automation, and measuring improvements in productivity and outcomes.

Develop and maintain MCP tools and the MCP Toolbox for Databases; expand test coverage for agentic coding assistants; refactor and maintain test frameworks; convert technical posts into production-ready sample code and documentation; build real-world data applications (QueryData) to support product R&D and collaborate with cross-functional teams to accelerate agentic AI adoption.

Design, build, and maintain AI-powered applications and automation workflows; integrate AI with enterprise systems, databases, and APIs; maintain legacy and Ruby production systems connected to Snowflake; develop ETL pipelines and reports; work in Linux environments, manage remote systems, reverse engineer undocumented systems, and collaborate with BI, analysts, and stakeholders to deliver practical, business-focused AI solutions.

Lead company-wide AI use case lifecycle from intake to production: assess workflows, scope solutions, run pilots, govern vendor/tool stack, measure business impact, and report outcomes to leadership.

Lead the AI Center of Excellence to define enterprise AI architecture and roadmap, design and implement GenAI and agentic solutions across cloud platforms, integrate AI with Oracle and Salesforce systems, establish ModelOps/security/governance best practices, run POCs and production rollouts, mentor teams, and collaborate with stakeholders to deliver scalable intelligent business systems.

Lead design and implementation of scalable, secure Python-based distributed systems and AI/LLM agentic workflows. Own features from design through production, remediate technical debt, ensure testability, security (SCA), observability, and collaborate with product and engineering leadership.

As a Sr. Fullstack Engineer, you will design and develop scalable applications using C# and .NET for backend and React for frontend, while integrating AI technologies.

Lead design and implementation of automated underwriting for consumer and small-business loans under $1M. Translate credit requirements into technical specs, prototype AI/ML and automation solutions, and drive cross-functional change, stakeholder buy-in, and executive presentations to scale intelligent decisioning workflows.

Build and maintain Python/FastAPI backend services and APIs, implement React frontend components, integrate MongoDB and AWS, collaborate via GitHub, participate in agile sprint planning, and work with AI and product teams to improve scalability and reliability.

This role involves developing strategies for electrical hardware design, consulting on new product development, mentoring junior staff, and leading innovative projects in HPC and AI systems.

Perform outbound prospecting (cold calls, email, LinkedIn) to identify and qualify B2B AI SaaS leads, book discovery calls and demos, maintain CRM records, collaborate with marketing and sales, hit monthly KPIs, and stay current on AI/SaaS trends.

Lead design, build, and scale client-facing and internal automation systems. Create advanced workflows (GoHighLevel, n8n/Make/Zapier), integrate platforms via APIs/webhooks, document SOPs, advise clients, maintain CRMs and troubleshoot live systems for reliability and scalability.

Outbound-focused SDR role selling AI SaaS: prospect via calls, email, and LinkedIn; qualify leads, engage decision-makers, book demos for AEs; maintain CRM records and meet monthly KPIs while collaborating with marketing and sales.