Top Machine Learning Jobs in Mexico

Develop, deploy, and monitor production ML models and infrastructure for fraud detection across global markets. Own feature engineering, model lifecycle, scalable pipelines, and collaborate with fraud, product, engineering, and risk teams to improve model performance and decisioning.

Design, train, and evaluate machine learning algorithms on high-dimensional data to produce practical predictive models. Improve NLP and machine perception using random forests, deep learning, and generative modeling; iterate experiments and translate findings into deployable applications.

Design, build, and maintain production-grade ML systems and AI agents for large-scale natural language data. Prototype and evaluate models, collaborate with cross-functional teams, and deliver data-driven solutions addressing legal and business problems.

The Machine Learning Analyst collaborates with AI/ML teams to identify data requirements, implements data ingestion pipelines, and ensures data quality while supporting data scientists in model development.

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

Build and operate scalable, secure ML/LLM infrastructure on Azure using Terraform and Azure DevOps. Implement end-to-end ML pipelines, manage model lifecycle, deploy inference services (AKS/Functions), integrate vector DBs and orchestration tools, and collaborate with data science and product teams to productionize generative AI features.

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.

Design, build, and maintain scalable data and ML pipelines, manage vector stores and RAG workflows, integrate LLM frameworks for training and inference, enable semantic search and AI-driven BI, and document data processes while partnering with stakeholders.

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.

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

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

The Data Scientist will develop machine learning models, apply optimization techniques, analyze complex datasets, and collaborate with cross-functional teams to deliver solutions tailored to life sciences and related industries.

The Senior AI/ML Engineer will architect and develop AI/ML solutions, collaborate with teams, and ensure deployment compliance, especially in healthcare.

The role involves designing, developing, and deploying AI/ML solutions focusing on Retrieval-Augmented Generation. The engineer will optimize retrieval performance, build data pipelines, and collaborate within teams while staying updated on AI advancements.

The ML Engineer will design, build, and operationalize AI systems for unstructured data, developing ML pipelines and collaborating with Data Scientists and Engineers to deliver scalable AI solutions.

As an ML Engineer at HARMAN, you'll design and operate end-to-end machine learning systems for in-vehicle audio systems, ensuring optimal performance and continuous improvement through telemetry and model monitoring.

Lead architecture and delivery of production-grade ML solutions for industrial IoT: predictive maintenance, anomaly detection, time-series forecasting, and real-time edge/cloud inference. Build robust data pipelines, mentor engineers, enforce ML best practices, and align technical roadmaps with business outcomes.

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.

Lead design and implementation of agentic AI/ML solutions and modern software (APIs, microservices, CI/CD, streaming) on cloud platforms. Bridge legacy IBMi AS400 systems with modern AI, train and fine-tune models, oversee lifecycle, mentor engineers, set technical direction, and improve automation and testing across claims adjudication systems.

As a Senior Machine Learning Engineer, you will develop and maintain infrastructure for ML applications, collaborate across teams, and drive tool development for data processing and model deployment.