Top Artificial Intelligence Jobs in Mexico

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.

Lead AI security operations for models and platforms: operate AI security tooling (red teaming, guardrails, AI-SPM), integrate into SecOps, design secure AI architectures, enable secure ML lifecycle, support governance, reporting, training, and continuous improvement.

Lead AI Security Operations work to protect AI platforms, models, and data through continuous assessment, monitoring, testing, and response. Operate AI security tooling (red teaming, guardrails, model evaluation, AI-SPM), integrate findings into incident response, design secure AI deployment patterns, embed controls across the AI lifecycle, support governance and audits, produce SOPs and reports, and provide training and POCs to improve AI security posture.

As a Sr. Lead AI Engineer, you will develop the agent harness for supplier agents, focusing on platform design and cloud infrastructure deployment while collaborating with product and engineering teams.

Lead pre-sales solution design for enterprise clients: gather requirements, produce technical proposals, cost/effort estimates, high-level designs, and statements of work. Support sales and delivery teams, enable CX and cloud-native architectures, and expand into AI, automation, and data-driven solutions.

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.

As a Lead AI Engineer, you will build an AI harness for supplier agents, manage context, design sub-agent patterns, and debug production behaviors, collaborating with a senior engineer on architecture.

Own end-to-end operational health for payment partners in Mexico, monitoring KPIs, troubleshooting integrations, analyzing large datasets, and driving improvements in payment flows, routing, and partner performance. Lead RCAs, build dashboards, present executive BEAT reports, and support product and growth teams with data-driven insights and automation/AI initiatives.

Lead complex AI and analytics initiatives using Palantir Foundry and ML techniques. Partner with Finance and Lead Gen stakeholders to translate business needs into scalable data solutions, perform data modeling, mining, visualization, and implement analytic workflows into products while ensuring maintainability and adoption.

Lead implementation and maintenance of applications, investigate and resolve user issues, analyze data and trends to recommend improvements, define use cases, develop business requirements with stakeholders, drive solution delivery, mentor colleagues, and apply software development, SQL, ERP, AI, and C#/.NET knowledge within Agile frameworks.

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.

Owns the lifecycle of AI-powered analytics products supporting clinicians, from requirements and discovery through production and maintenance. Partners with data scientists, engineers, clinicians, and executives to define roadmaps, prioritize trade-offs, establish data contracts and SLAs, validate model results, track performance, and demonstrate ROI. Leads Agile scrum teams and drives cross-functional delivery in complex healthcare environments.

Build and operate production AI systems for client organizations, including agent runtimes, model routing, RAG, tool calling, guardrails, evaluation, monitoring, and cost and latency optimization. The role turns AI prototypes into reliable, measurable, safe production capabilities while collaborating with product, engineering, platform, and security teams in a client-facing consulting environment.

Drive architecture governance and infrastructure standards while championing developer experience through reusable patterns and accelerators. Lead cross-functional efforts to improve deployment pipelines and system integration, and provide documentation and enablement for software engineering teams. The role requires expertise in software architecture, DevOps, DevSecOps, platform engineering, Azure architecture, and developer tooling. AI/GenAI integration, multi-tenant SaaS, cloud-native foundations, and AI coding assistants are desired.

Design and deliver production-grade applied AI solutions (LLM workflows, RAG, agentic automation). Lead client discovery, define specs for prompt engineering, measure model quality/ROI, advise on data and safe deployment, and contribute reusable AI assets.

Design, build, test, and iterate prompts and prompt chains for production LLM workflows. Implement evaluation harnesses, guardrails, and regression tests. Integrate LLM components with APIs, RAG pipelines, and structured outputs. Tune for accuracy, cost, latency, and safety. Partner with AI Specialists, document reusable patterns, and grow a library of AI accelerators.

Designs and operates production data platforms supporting warehouses, streaming and batch pipelines, semantic layers, RAG systems, embeddings, vector stores, and AI feature pipelines. Responsibilities include dimensional modeling, data contracts, lineage, observability, testing, governance, cost optimization, CI/CD, cloud infrastructure, and API delivery. Partners with data scientists and stakeholders, mentors engineers, and ensures reliable, secure, and maintainable data systems.

Designs domain AI architecture and developer experience solutions. Responsibilities include software engineering, cloud and platform architecture on Azure, DevOps and DevSecOps, developer tooling, reusable patterns, architecture governance, documentation, enablement, and cross-functional collaboration. AI, generative AI, and multi-tenant SaaS experience are preferred. The role is remote and requires advanced English proficiency.

Lead architecture and development of AI-first backend systems and orchestration for Audit Suite. Drive AI integration (LLMs, retrieval, vector stores), ensure scalability, reliability, observability, and compliance. Mentor engineers, lead cross-functional initiatives, shape roadmap and platform integration, and implement high-throughput, low-latency AI workflows across heterogeneous stacks.

Lead design, build, and productionization of GenAI solutions and traditional full-stack applications. Scope use cases, engage stakeholders, mentor developers, manage roadmaps, ensure security/compliance, and operate deployed systems with continuous improvement.

Build the technical foundation for a new business vertical, including reusable infrastructure, integrations, workflows, and tooling. Lead early customer engagements from technical scoping through delivery and support, translate customer needs into durable systems, and partner with Product and Engineering to productize repeatable capabilities. The role requires strong backend and data engineering instincts, comfort with ambiguity, and the ability to balance rapid delivery with long-term architecture.

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 and operate AI platforms, tooling, infrastructure, and machine learning development environments across AstraZeneca. Collaborate with data scientists and machine learning engineers, automate lifecycle tasks, improve scalable workflows, adapt machine learning methods for distributed and GPU environments, and transition research into production. Partner with cybersecurity and data privacy teams to maintain secure, compliant systems while enabling productivity.

Build and operate AI and machine learning platforms, tooling, and infrastructure across AstraZeneca. Collaborate with data scientists and ML engineers to improve development environments, automate workflows, scale research using distributed and GPU computing, and transition exploratory work into production. The role also partners with cybersecurity, data privacy, and governance teams to maintain secure, compliant platforms while enabling productivity.

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.