Top Machine Learning Jobs in Mexico City

As an AI/ML Engineer, design and implement AI solutions, collaborate with teams, build ML pipelines, and ensure quality and compliance in healthcare applications.

Create and review realistic, locale-specific robotics and natural sciences tasks in Spanish (LATAM), apply scoring rubrics, evaluate AI and human responses for accuracy and regulatory alignment, and produce gold-standard solutions and expert feedback.

Operate and monitor AI models and agent systems in production. Track performance, drift, latency, errors, availability, and business impact; manage incidents, rollbacks, deployments, versioning, registries, and lifecycle transitions. Maintain governance, audit trails, risk controls, fairness, bias, explainability, and transparency monitoring. Integrate cloud, container, monitoring, logging, alerting, and CI/CD platforms while collaborating with product, engineering, data science, and risk teams.

Design, build, and maintain scalable data pipelines, vector stores, feature stores, and LLM-driven workflows. Implement RAG, prompt engineering, model fine-tuning, and managed LLM deployments on cloud. Optimize big-data processing, enable real-time inference, build analytics/NL query dashboards, document processes, and partner with stakeholders to translate requirements into production BI/ML solutions.

The role involves developing and maintaining a robust ML/AI platform, focusing on infrastructure, autonomous agents, CI/CD pipelines, and AI-native tooling, with responsibilities across engineering best practices.

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.

Build benchmarks and eval infrastructure for real-world AI evaluation. Partner with researchers and customers to design benchmarks, process multimodal data, build data pipelines, sandboxed execution environments, storage, and orchestration. Ship iterative eval infrastructure and own engineering for customer engagements.

Design, train, and refine machine learning algorithms on high-dimensional data to produce practical predictive models. Apply random forests, deep learning, generative modeling, and neural-network techniques to improve NLP and machine perception for real-world applications.