Top Artificial Intelligence Jobs in Mexico City

Design performance creatives, landing pages, lifecycle journeys, and conversion flows for acquisition across three Latin American markets. Use generative AI and automation tools to accelerate production, build scalable Figma systems, maintain brand consistency, and support creative testing. Partner with Paid Media to analyze performance metrics, develop hypotheses, and optimize assets through A/B testing. Scout emerging AI tools, document workflows, and manage multiple formats and languages in parallel.

The Freelance AI Artist will produce high-quality images and videos using AI technologies, collaborating with marketing to align with brand identity and experimenting with new workflows.

Lead design and implementation of a scalable, secure multi-tenant SaaS using .NET and Angular. Own full-stack delivery from domain modeling through frontend, automated testing, CI/CD, and production support while mentoring engineers and leveraging AI-assisted development to accelerate quality delivery.

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.

Owns AI and machine learning model and agent behavior from problem framing and algorithm selection through fine-tuning, retrieval, orchestration, evaluation, and production serving. The role requires deep expertise in machine learning, neural networks, statistics, agent memory, knowledge graphs, cloud deployment, and production model lifecycle tooling. Partners with data engineering while leading algorithm, prompting, and evaluation decisions, with opportunities to guide technical discovery and client workshops.

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.

Build and deploy production software for B2B fintech customers, owning features from design through monitoring and maintenance. Engineers may focus on backend-leaning product development or data and machine learning systems for risk, fraud, and spend intelligence. The role emphasizes AI-native development practices, strong software fundamentals, collaboration with Product and Design, ownership, and rapid learning in a multicultural environment.

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.

Build production LLM agents, multi-step workflows, MCP servers, evaluation and observability systems, and document-extraction pipelines. Architect AI-ready semantic data layers over Snowflake and dbt, develop Python ingestion and Airflow workloads, and implement retrieval, embeddings, governance, security, and reliable agent operations. Lead technical design, mentor peers, and advise clients in an embedded consulting engagement.

Design, build, and deploy bespoke AI/LLM-driven solutions on the Salesforce platform. Handle end-to-end delivery including data modeling, integration, agent orchestration, debugging, prototyping, and client-facing implementation and optimization.

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.

Lead IT operations across six Latin American countries, owning strategy, cloud and infrastructure, cybersecurity governance, service delivery, budgets, vendor management, and regional IT team development.

Design and implement AI-powered data pipelines and production workflows using LLMs, automation, and modern data engineering. Build systems that extract, clean, enrich, validate, and transform structured and unstructured data into customer-ready Excel, PDF, and Word outputs. Develop APIs and full-stack applications, optimize relational and vector databases, integrate AI orchestration frameworks, and translate prototypes into scalable enterprise solutions.

Design and build AI-powered IT help desk automation using AWS Bedrock and serverless tech. Develop AI agents for ticket triage, routing, IAM automation, REST integrations with enterprise systems, and scalable cloud-native architectures. Collaborate on architecture, integrations, and agent performance improvements.

Lead design and evolution of scalable, secure cloud-native SaaS platforms using C#/.NET. Define architecture standards, champion microservices, DDD, CQRS, event-driven and API-first approaches, mentor engineers, improve performance, observability, and security, and integrate AI-assisted development tools and workflows across teams.

Design difficult open-web research problems for benchmarking frontier AI browsing agents. Responsibilities include constructing multi-step questions, validating clues across databases, archives, registries, and PDFs, documenting auditable evidence trails, recording search validation results, testing research difficulty, resolving ambiguity, and delivering reproducible research documentation.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Supports software engineering teams through technical guidance, code reviews, mentoring, requirements definition, and project execution. Helps shape engineering strategy, assess technologies, prioritize tasks, monitor timelines and budgets, and mitigate risks. Promotes collaboration, innovation, and continuous learning while contributing to software product development and team growth.

Designs and implements AI agent architectures for sales workflows, including lead qualification, deal coaching, proposal generation, competitive intelligence, and pipeline analytics. Owns the product roadmap, backlog, user stories, acceptance criteria, and success metrics. Builds multi-agent integrations with Salesforce and data platforms, establishes standards for prompting and responsible AI, and leads sprint planning and release management while aligning technical delivery with sales business outcomes.

Develop, test, debug, and support software with emphasis on complex automation components. Collaborate on requirements, design and code reviews, troubleshoot production issues, evaluate technologies, solve technical challenges, and mentor junior engineers. Provide subject matter expertise and contribute to software development best practices.

Leads DXC’s AI-first sales transformation by designing autonomous agents, generative AI solutions, and automation to replace inefficient sales processes. Prototypes and tests MVPs with sellers, evaluates emerging AI technologies, develops the 12–24 month AI-native sales roadmap, and drives organizational adoption. The role partners with sales leadership to identify high-impact opportunities and communicates a compelling vision for transformational revenue improvements.