Top Artificial Intelligence Jobs in Mexico City

The AI Developer will design, train, optimize, and deploy LLM models, develop backend services and maintain models in offline environments, focusing on ML engineering and optimizing workflows.

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 and own client-specific QA programs for generative AI data: set QA strategies, run root-cause analyses and CAPA, plan rater training and certification, maintain dashboards, manage escalations, standardize SOPs, pilot automations, coach raters, and ensure secure data handling across distributed teams.

Lead frontend architecture for Clara Builders (Next.js 15, React, TypeScript), extend backend/infra (Prisma/Postgres, deploy pipeline), work on the Cloudflare AI Gateway and agent infrastructure (Figma-to-code MCP, documentation agents, n8n automations), design and ship new AI tooling (OAuth clients, unified access), and raise technical standards across the pod through ownership and code review.

Lead and coordinate cross-functional value-creation and AI transformation initiatives: define measurable use cases, build KPI dashboards and business cases, track execution and benefits, support operating-model and SI transitions, and prepare executive-ready reporting to drive adoption and measurable business impact.

Build and operate distributed backend services and genomic reasoning engines that combine whole genome bioinformatics with AI. Design scalable data models, retrieval and vector search systems, and deterministic guardrails to ensure scientifically accurate, explainable genomic AI for production use.

Design and build production AI systems and distributed backend services that enable genomic reasoning, retrieval, and data platforms. Integrate genomic annotations and interpretation logic, architect high-performance data models and storage across PostgreSQL, DuckDB, Qdrant, Redis, and Spark. Ensure AI outputs are accurate, explainable, and grounded in genomic evidence while collaborating across engineering and bioinformatics teams.

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.

Design and implement scalable Generative AI architectures, build Python services, integrate LLMs, oversee deployment operations, and evaluate emerging technologies.

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.

Lead AI validation strategy for LLMs, RAG, and agent systems. Build automated CI/CD-integrated evaluation pipelines, design semantic and prompt regression tests, implement guardrails and confidence scoring, monitor production AI quality, and define metrics/KPIs in partnership with engineering and governance teams.

Use AI tools and prompt engineering to accelerate software development: create context-rich prompts, generate and validate code, designs, tests, and documentation; analyze requirements, decompose problems, navigate large codebases, identify defects, and deliver production-ready solutions while ensuring security, scalability, and coding standards.

Support Central Operations by evaluating, implementing, and maintaining AI-driven automations and low-code tooling (e.g., Zapier). Build unified dashboards to track AI agent performance, drive internal adoption, document automations, run LLM evaluation/monitoring for human-in-the-loop workflows, and collaborate with product, engineering, and ops to reduce manual work and improve reporting.

Support Central Operations by evaluating, implementing, and maintaining AI agents and low/no-code automations (e.g., Zapier). Build unified dashboards to track AI agent performance, reduce manual work, document automations, run LLM evaluation/monitoring, train stakeholders, and provide feedback to align AI with business workflows.

Lead the product strategy for Scribd's internal developer and agentic platforms: unify AI workstreams, own internal app/plugin/agent runtimes, translate functional needs into platform requirements, drive cross-functional adoption and metrics, and run internal rollouts and evaluation to prove impact.

As a Lead Software Engineer at PMG, you will design, develop, and deploy scalable AI-driven solutions, mentor other engineers, and enhance the Alli platform for digital marketing. Responsibilities include coding, code reviews, and integration with cross-functional teams.

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.

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.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

As an AI Software Engineer, you will develop Generative AI solutions, manage full-cycle development, implement MLOps pipelines, and ensure compliance with AI standards.

Teach and mentor experienced engineers transitioning to architecture roles through live sessions, reviews, architecture boards, mock interviews, and recorded lessons. Provide detailed, actionable architectural feedback, lead office hours, assess projects, and oversee supporting instructors to maintain high technical standards.

Full-time (40 hrs/week) remote instructor role to run live lessons and Q&A, provide 1:1 mentoring, answer student messages, review projects, and give actionable feedback to help students become professional AI software engineers.