Top Artificial Intelligence Jobs in Mexico City

Four-day remote bootcamp for Latin American candidates to learn AI-powered commerce, social commerce, e-commerce, media, and customer engagement through practical challenges, team exercises, and competitions. Top performers may be considered for future roles.

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 Applied AI Engineer will develop and deploy AI automation solutions, integrate LLMs, and collaborate with clients to enhance workflows and systems.

As an Agentic AI Solutions Architect, lead technical client interactions, design data and AI architectures, and implement automation solutions, ensuring effective use of AI. Collaborate with clients to optimize operations and deliver measurable ROI through innovative technology solutions.

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.

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.

Design, build, and maintain end-to-end GenAI systems using Python/FastAPI backends and React/TypeScript frontends on GCP Vertex AI. Lead LLM orchestration, observability, cost optimization, production support, and mentor teammates while collaborating on architecture, deployments, and monitoring.

Design, architect, and deliver enterprise-grade generative AI solutions (LLMs) for healthcare. Build end-to-end ML pipelines, scalable cloud deployments, APIs and integrations, prompt engineering, RAG and conversational systems. Ensure responsible AI, privacy compliance, performance optimization, and cross-functional collaboration while documenting architectures and best practices.

The Lead AI Forward Engineer designs and delivers AI-powered solutions, guiding teams in automation, architecture design, and implementation. Responsibilities include system design, technology strategy, and cross-functional partnership to meet business needs effectively.

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.

Lead technical client engagements in machine learning and AI, shape strategy, influence architecture, and ensure high-quality outcomes for clients by leveraging AWS services.

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.

The AI Engineer & Data Engineer will develop data pipelines, ensure data quality, and integrate AI into workflows for performance reporting and billing reconciliation.

Design, implement, and maintain automated test suites using Playwright. Integrate tests into CI/CD pipelines, mentor team members, and enhance test reliability.

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

As an AI Data Engineer, you will build end-to-end data pipelines, collaborate on AI features, and ensure data quality across our systems.

Optimize Forcepoint.com conversion and engagement using behavioral analytics, A/B and multivariate testing, personalization, and AI-assisted workflows. Analyze visitor behavior, build experiments and personalized experiences, improve content and machine readability, and report impacts on leads, pipeline and business outcomes.

Manage and automate enterprise AI platform administration (provisioning, RBAC, billing), run the Innovation teams operating rhythm, keep roadmap current, produce weekly AI intelligence briefs, support SVP of Innovation with prioritization and coordination, and grow into a strategic voice.

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.

Draft, adapt, and annotate commercial agreements (MSAs, leases, IP licences, marketing agreements). Ensure documents reflect current standards, explain clauses and risks, respond to feedback, and deliver non-confidential materials suitable for AI training.

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.

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, build, and operate production AI systems, enhance backend services, improve reliability, and collaborate on technical projects within the AI-native engineering team.