Top Artificial Intelligence Jobs in Mexico City

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.

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.

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.

The Senior Product Manager for AI will lead the AI transformation at Aplazo by owning the AI roadmap, delivering AI-powered features, and developing internal tools, all while collaborating with cross-functional teams.

Own Aleph’s shared AI foundation, including model selection and routing, proxy infrastructure, context management, tool design, and evaluation systems. Ship production agentic features using prompt engineering, caching, parallel tool calls, and subagents. Build observability for agent performance, collaborate with customers and finance experts to define quality standards, and drive adoption of reliable AI systems across product teams.

Industry Expert who reviews advanced AI and systems engineering projects, leads technical defenses and mock architecture review boards, hosts occasional design sessions, and sets technical standards for instructors. The role requires senior-level production engineering experience, strong architectural judgment, the ability to critique systems live, and expertise in AI/ML engineering, AI systems, or forward-deployed engineering. This is a fully remote commitment of 4–10 hours monthly with negotiable hourly compensation.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

The AI & Software Engineering Senior Manager directs a team in developing innovative digital marketing solutions, manages product roadmaps, and drives AI-powered projects for high-impact results in the media and advertising sector.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers supporting agency, client, BI, product, and AI consumers. Integrate fragmented marketing and customer data across advertising platforms, e-commerce, analytics, and CRM systems. Manage dbt projects, multi-tenant Snowflake infrastructure, data quality, performance, and client-specific modeling. Use AI coding agents, cloud infrastructure, CI/CD, and automated testing while collaborating across product, engineering, analytics, and client teams.

Design, build, and operate scalable cloud backend services that power LLM-driven features (inference, RAG, agents). Own end-to-end delivery including IaC (Terraform), CI/CD, monitoring, and incident response; collaborate across product, UX, and platform teams.

Serve as a client-facing support engineer for a fintech SaaS platform, triaging and resolving issues, using AI tools to enhance responses, logging and escalating tickets in Jira, executing basic corrective actions, and driving change requests from recurring feedback.

The role involves developing full-stack applications for mental healthcare, utilizing TypeScript, Node.js, React, and AI tools. Candidates will own features end-to-end and collaborate with teams to enhance user experience and system performance.

Design, build, and deploy end-to-end LLM-based AI applications (RAG, fine-tuning, embeddings). Work with founders and clients, iterate rapidly, and own production delivery including cloud deployment and ML pipelines. Occasionally contribute to frontend, dev tooling, or data pipelines.

Embed with business teams to rapidly prototype, build, and ship AI-powered workflows: prompt layers, automations, lightweight agents, RAG, and API integrations. Translate workflow assessments into deployable solutions, run UAT, document handoffs, and feed learnings back to the AI Program Office.

Leads enterprise sales strategy and revenue growth for AI-powered eDiscovery, legal technology, compliance, analytics, and data intelligence solutions. Builds pipelines, manages complex consultative sales cycles, develops executive client relationships, expands strategic accounts, and positions responsible AI adoption. Partners with product, operations, legal, security, marketing, and executive teams on solution development, pricing, contracts, implementation, and market expansion across corporations, law firms, government, and regulated industries.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Design and implement AI-powered business workflows using Microsoft Copilot Studio and Power Platform (Power Apps, Power Automate, AI Builder). Build automations, conversational experiences, and integrations with SharePoint and REST APIs. Collaborate with solution architects and technical leads to support implementation, integration, testing, and deployment across enterprise processes.

Lead architecture and evolution of Juniper Square’s distributed services, cloud infrastructure, and developer platform. Design scalable, multi-tenant, event-driven systems; build paved-road developer tooling, CI/CD, and observability. Hands-on coding, prototyping, and performance troubleshooting. Enable production AI/LLM workloads, ensure security/compliance, support enterprise upmarket needs, and mentor engineering teams to raise operational excellence and reliability.

Lead identification, design, deployment, and measurement of AI-powered automations across operations. Map high-friction workflows, build agent- or rule-based automations, create Metabase dashboards, partner with Product/Engineering to instrument data, and document solutions for handoff.

Ship real open-source integrations and MCP servers to bring SearchApi to agent and automation frameworks. Create PRs, example apps, docs, and integration guides. Stay current with the agent ecosystem, pick high-leverage targets, and work independently to deliver production-ready integrations.

As a Customer Support Expert, you will respond to customer inquiries via social media and email, providing excellent service on behalf of various brands.

Lead a team of ML engineers, manage client engagements, and provide strategic advisory on ML architecture while driving pre-sales. Ensure team growth and quality of deliverables.